Triple
T16613355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acropyga |
E403630
|
entity |
| Predicate | stingingApparatus |
P110493
|
FINISHED |
| Object | sting absent |
—
|
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: sting absent | Statement: [Acropyga, stingingApparatus, sting absent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stingingApparatus Context triple: [Acropyga, stingingApparatus, sting absent]
-
A.
hasStinger
chosen
Indicates that an entity possesses a stinger as a physical feature.
-
B.
describesApparatus
Indicates that one entity provides a description or specification of an apparatus used by another entity or within a particular context.
-
C.
Tornado ADV
Indicates that the action or event occurs in the manner of, or under conditions characterized by, a tornado (e.g., violently, turbulently, or with tornado-like intensity).
-
D.
hasArrestingGear
Indicates that an entity is equipped with a system or mechanism used to rapidly decelerate and stop another entity, typically during landing or capture.
-
E.
fireEnginesUsed
Indicates that certain fire engines were employed or deployed in carrying out a particular firefighting operation or incident.
- 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_69e360983d2c8190b1fe7f18aedfbde1 |
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.