Triple
T24942731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | silvered leaf monkey |
E623502
|
entity |
| Predicate | adultFurDescription |
P127605
|
FINISHED |
| Object | long silvery-tipped hair |
—
|
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: long silvery-tipped hair | Statement: [silvered leaf monkey, adultFurDescription, long silvery-tipped hair]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adultFurDescription Context triple: [silvered leaf monkey, adultFurDescription, long silvery-tipped hair]
-
A.
hasFurType
Indicates that an entity possesses a specific kind or texture of fur.
-
B.
hasFur
Indicates that an entity possesses fur as a physical covering on its body.
-
C.
adultMaleColoration
Indicates the characteristic coloration pattern specifically exhibited by adult males of a species.
-
D.
adultStageCharacteristic
chosen
Indicates that the specified characteristic is exhibited during the adult life stage of an organism.
-
E.
femaleFigure2Attribute
Indicates that a female figure is associated with a particular attribute or characteristic.
- 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f423dbea9881909b32c92c4f1eda9a |
completed | May 1, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:31 a.m.