Triple
T5690416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siberian ibex |
E125414
|
entity |
| Predicate | femaleHas |
P65966
|
FINISHED |
| Object | smaller horns |
—
|
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: smaller horns | Statement: [Siberian ibex, femaleHas, smaller horns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleHas Context triple: [Siberian ibex, femaleHas, smaller horns]
-
A.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
-
B.
hasFemaleSpeaker
Indicates that the associated content, event, or communication is spoken or narrated by a female individual.
-
C.
femaleBehavior
Indicates that the behavior or actions being referred to are characteristic of, or typically associated with, females in the given context.
-
D.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
E.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c028fec2bc819083f5dca6a8d9d435 |
completed | March 22, 2026, 5:38 p.m. |
Created at: March 22, 2026, 3:44 p.m.