elp(propositions): native-el READ primitive — memory text -> SACRED triples

Phase 3 piece 1. Ports propositions.py off spaCy: the dependency-parser role is
now the el-native parser (parse_spec), and each memory sentence's meaning-spec
IS the triple (subject, predicate, object, modifiers, polarity, tense, source,
confidence). Sentence segmentation + repr parity with propositions.py. NEGATION
SACRED: polarity flows straight from the spec, never dropped/inverted.

Gate (propositions_gate.el): 4/4 SACRED polarity correct on extraction;
multi-sentence memory splits one triple per sentence in reading order with
negation preserved. Built bounded (elc rc=0 peak 24MB, cc rc=0).

Gap (honest): English regular-verb lemmatizer does not restore silent-e
(stores->stor); coreference/passive normalization from the reference not yet
ported (shallow pronoun subject kept as surface).

elp/src/propositions.el

@@ -0,0 +1,140 @@
+// propositions.el - the READ primitive over the engram's OWN memories, native el.
+//
+// Free memory text -> structured PROPOSITIONS (triples):
+//     (subject, predicate, object, modifiers, polarity, tense, source, confidence)
+//
+// This is comprehension turned inward: the Python reference (propositions.py) ran
+// spaCy's dependency parser over each memory sentence and walked the arcs. Here
+// the spaCy role is filled by the el-native parser (comprehend.el / parse_spec):
+// each sentence is parsed to a meaning-spec, and the spec's roles ARE the triple.
+// Nothing generates text. NEGATION IS SACRED: polarity flows straight from the
+// spec's polarity field and is never dropped or inverted.
+//
+// Depends on: comprehend (parse_spec / parse_spec_lang), grammar (slots_get).
+
+// ── sentence segmentation ─────────────────────────────────────────────────────
+// Split on sentence-final punctuation (. ! ?) and hard newlines. Markdown/long
+// memories are handled shallowly (the reference caps + ranks by query overlap;
+// that ranking belongs to the dialogue layer, not here).
+
+fn prop_is_boundary(c: String) -> Bool {
+    if str_eq(c, ".") { return true }
+    if str_eq(c, "!") { return true }
+    if str_eq(c, "?") { return true }
+    if str_eq(c, "\n") { return true }
+    return false
+}
+
+fn prop_split_sentences(text: String) -> [String] {
+    let out: [String] = native_list_empty()
+    let n: Int = str_len(text)
+    let start: Int = 0
+    let i: Int = 0
+    while i < n {
+        let c: String = str_slice(text, i, i + 1)
+        if prop_is_boundary(c) {
+            let seg: String = str_slice(text, start, i + 1)
+            let trimmed: String = cp_trim_punct(seg)
+            if !str_eq(trimmed, "") {
+                let out = native_list_append(out, seg)
+            }
+            let start = i + 1
+        }
+        let i = i + 1
+    }
+    if start < n {
+        let seg: String = str_slice(text, start, n)
+        let trimmed: String = cp_trim_punct(seg)
+        if !str_eq(trimmed, "") {
+            let out = native_list_append(out, seg)
+        }
+    }
+    return out
+}
+
+// ── spec -> proposition record ────────────────────────────────────────────────
+// A proposition is a slot map (same [String] shape as the spec) with the READ
+// contract keys. Modifiers fold the spec's location + iobj adjuncts.
+
+fn prop_confidence(subject: String, predicate: String, object: String) -> String {
+    if str_eq(predicate, "") { return "0.0" }
+    if str_eq(subject, "") { return "0.4" }
+    if str_eq(object, "") { return "0.7" }
+    return "1.0"
+}
+
+fn prop_modifiers(spec: [String]) -> String {
+    let loc: String = slots_get(spec, "location")
+    let iobj: String = slots_get(spec, "iobj")
+    let parts: [String] = native_list_empty()
+    if !str_eq(loc, "") { let parts = native_list_append(parts, loc) }
+    if !str_eq(iobj, "") { let parts = native_list_append(parts, "to " + iobj) }
+    return str_join(parts, "; ")
+}
+
+fn prop_from_spec(spec: [String], source_id: String) -> [String] {
+    let subject: String   = slots_get(spec, "agent")
+    let predicate: String = slots_get(spec, "predicate")
+    let object: String    = slots_get(spec, "patient")
+    let polarity: String  = slots_get(spec, "polarity")
+    let tense: String     = slots_get(spec, "tense")
+    let mods: String      = prop_modifiers(spec)
+    let conf: String      = prop_confidence(subject, predicate, object)
+
+    let p: [String] = native_list_empty()
+    let p = native_list_append(p, "subject");    let p = native_list_append(p, subject)
+    let p = native_list_append(p, "predicate");  let p = native_list_append(p, predicate)
+    let p = native_list_append(p, "object");     let p = native_list_append(p, object)
+    let p = native_list_append(p, "modifiers");  let p = native_list_append(p, mods)
+    let p = native_list_append(p, "polarity");   let p = native_list_append(p, polarity)
+    let p = native_list_append(p, "tense");      let p = native_list_append(p, tense)
+    let p = native_list_append(p, "source");     let p = native_list_append(p, source_id)
+    let p = native_list_append(p, "confidence"); let p = native_list_append(p, conf)
+    return p
+}
+
+// Extract one proposition from a single sentence (given language).
+fn prop_extract_one_lang(sentence: String, lang: String, source_id: String) -> [String] {
+    let spec: [String] = parse_spec_lang(sentence, lang)
+    return prop_from_spec(spec, source_id)
+}
+
+fn prop_extract_one(sentence: String, source_id: String) -> [String] {
+    return prop_extract_one_lang(sentence, "en", source_id)
+}
+
+// Render a proposition as a compact trace line (repr parity with propositions.py).
+fn prop_repr(p: [String]) -> String {
+    let neg: String = ""
+    if str_eq(slots_get(p, "polarity"), "neg") { let neg = "NOT " }
+    let mods: String = slots_get(p, "modifiers")
+    let modstr: String = ""
+    if !str_eq(mods, "") { let modstr = " [" + mods + "]" }
+    let s: String = "(" + slots_get(p, "subject") + " -" + neg + slots_get(p, "predicate")
+    let s = s + "-> " + slots_get(p, "object") + modstr
+    let s = s + " conf=" + slots_get(p, "confidence") + ")"
+    return s
+}
+
+// Extract all propositions from a memory's text (one per sentence). Returns a
+// flat [String] whose entries are the prop_repr trace lines, in reading order.
+fn prop_extract_lang(text: String, lang: String, source_id: String) -> [String] {
+    let sents: [String] = prop_split_sentences(text)
+    let m: Int = native_list_len(sents)
+    let out: [String] = native_list_empty()
+    let i: Int = 0
+    while i < m {
+        let sent: String = native_list_get(sents, i)
+        let p: [String] = prop_extract_one_lang(sent, lang, source_id)
+        // drop empty parses (no predicate recovered): honest partial, not noise.
+        if !str_eq(slots_get(p, "predicate"), "") {
+            let out = native_list_append(out, prop_repr(p))
+        }
+        let i = i + 1
+    }
+    return out
+}
+
+fn prop_extract(text: String, source_id: String) -> [String] {
+    return prop_extract_lang(text, "en", source_id)
+}