Triple
T122419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIG |
E2477
|
entity |
| Predicate | hasPluralForm |
P5088
|
FINISHED |
| Object | SIGs |
—
|
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: SIGs | Statement: [SIG, hasPluralForm, SIGs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPluralForm Context triple: [SIG, hasPluralForm, SIGs]
-
A.
hasPlurality
Indicates that an entity or concept exists or is expressed in a plural form rather than a singular one.
-
B.
hasFullForm
Indicates that one entity is the complete, expanded, or unabbreviated form of another entity.
-
C.
hasGrammaticalGender
Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
-
D.
hasExonym
Indicates that one entity is known by an alternative name or designation in another language or cultural context.
-
E.
hasOppositeNumberForm
Indicates that one entity is represented by a number form that is the opposite (e.g., additive vs. subtractive, positive vs. negative, or otherwise contrastive) of the number form used to represent the other entity.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a2573b4e7481909ee09d2899f8a74b |
completed | Feb. 28, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69a2564928208190966a619680a0d6e2 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256c72f6c81909b619b90d829d86e |
completed | Feb. 28, 2026, 2:45 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.