Triple
T31463988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexican Sign Language |
E802674
|
entity |
| Predicate | hasNonManualGrammar |
P202645
|
FINISHED |
| Object | facial expressions |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: facial expressions | Statement: [Mexican Sign Language, hasNonManualGrammar, facial expressions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNonManualGrammar Context triple: [Mexican Sign Language, hasNonManualGrammar, facial expressions]
-
A.
hasGrammar
Indicates that an entity possesses, follows, or is associated with a particular system of grammatical rules or structure.
-
B.
hasGrammarStatus
Indicates that an entity is associated with a particular grammatical status or classification (e.g., tense, mood, aspect, or correctness).
-
C.
hasGrammarFrom
Indicates that one entity derives or uses its grammatical structure or rules from another entity.
-
D.
hasStandardizedGrammar
Indicates that a language or notation follows an officially defined and consistently applied set of grammatical rules.
-
E.
hasDocumentedGrammars
Indicates that there exist written or otherwise formally recorded grammars describing the linguistic structure of the subject language or linguistic system.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f348c84c1c81908739f100ecf7394e |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a00a602b6b48190a9dea8ae22d2fa05 |
completed | May 10, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_6a00a559bf3881909a7b50776d8f47bb |
completed | May 10, 2026, 3:33 p.m. |
| PDg | Predicate description generation | batch_6a00a601ff988190b3eb7a9abc92a557 |
completed | May 10, 2026, 3:36 p.m. |
Created at: April 30, 2026, 9:22 p.m.