Triple
T16613163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anoplolepis |
E403626
|
entity |
| Predicate | recognitionFeature |
P51216
|
FINISHED |
| Object | workers often have long legs |
—
|
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: workers often have long legs | Statement: [Anoplolepis, recognitionFeature, workers often have long legs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recognitionFeature Context triple: [Anoplolepis, recognitionFeature, workers often have long legs]
-
A.
recognizedFeature
Indicates that a particular feature has been identified and acknowledged as present or valid in relation to an entity or context.
-
B.
signatureFeature
chosen
Indicates that one entity is a defining or characteristic feature that distinctly identifies or typifies another entity.
-
C.
recognizesPeople
Indicates that an entity is able to identify or acknowledge specific individuals as distinct persons.
-
D.
visualFeature
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
E.
currentRecognition
Indicates the recognition or acknowledgment an entity presently receives, such as awards, honors, or formal status it currently holds.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3609776d48190b6b8c7826ac575c4 |
completed | April 18, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.