Triple
T1412876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | greater kudu |
E31842
|
entity |
| Predicate | antiPredatorStrategy |
P26653
|
FINISHED |
| Object | freezing and camouflage in vegetation |
—
|
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: freezing and camouflage in vegetation | Statement: [greater kudu, antiPredatorStrategy, freezing and camouflage in vegetation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antiPredatorStrategy Context triple: [greater kudu, antiPredatorStrategy, freezing and camouflage in vegetation]
-
A.
preysOn
Indicates that one entity hunts, kills, and consumes another entity as a food source.
-
B.
apexPredatorIn
Indicates that an entity is the top predator within a specified environment, ecosystem, or location, facing no regular natural predators there.
-
C.
camouflageEffectiveness
Indicates how well one entity’s appearance or behavior conceals it from detection by another entity or sensing system.
-
D.
aimsToProtect
Indicates an intention or purpose to safeguard or defend one entity, value, or condition from harm, risk, or undesirable outcomes.
-
E.
defenseResult
Indicates the outcome or consequence of a defensive action or strategy in response to an attack or threat.
- 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_69a49919a994819086528951bc224775 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3e3383c81909acb9c6c1c3b817a |
completed | March 1, 2026, 10:55 p.m. |
| PD | Predicate disambiguation | batch_69a4bf048b648190ab77d9b45cb4855f |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf8158ac8190b8360ecccc2980bc |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:59 p.m.