Triple
T138964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sierra Nevada bighorn sheep |
E2809
|
entity |
| Predicate | sexuallyDimorphic |
P1333
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Sierra Nevada bighorn sheep, sexuallyDimorphic, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sexuallyDimorphic Context triple: [Sierra Nevada bighorn sheep, sexuallyDimorphic, true]
-
A.
sexualDimorphism
chosen
Indicates differences in physical characteristics between males and females of a species that are systematically associated with their sex.
-
B.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
C.
reproductiveType
Indicates the mode or strategy by which an organism reproduces or generates offspring.
-
D.
sexualOrientation
Indicates an entity’s enduring pattern of romantic or sexual attraction toward others, typically in terms of the genders or sexes to which it is attracted.
-
E.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
- F. None of above.
Provenance (3 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a800148190be119d1d075869b8 |
completed | Feb. 28, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69a2565426c08190aab68e34a6a2d60e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.