Triple
T28258350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidential baton of Argentina |
E712511
|
entity |
| Predicate | wornOrCarriedBy |
P84687
|
FINISHED |
| Object | incumbent President of Argentina |
—
|
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: incumbent President of Argentina | Statement: [Presidential baton of Argentina, wornOrCarriedBy, incumbent President of Argentina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wornOrCarriedBy Context triple: [Presidential baton of Argentina, wornOrCarriedBy, incumbent President of Argentina]
-
A.
wornAs
Indicates that one entity is used or put on as clothing, an accessory, or a wearable item by another entity.
-
B.
temporarilyWornBy
Indicates that an item is being worn by an entity for a limited or non-permanent period of time.
-
C.
oftenEquippedWith
Indicates that one type of entity is frequently or typically outfitted, furnished, or supplied with another entity.
-
D.
typicallyWornBy
chosen
Indicates that something (such as an item or garment) is most commonly or characteristically worn by a particular type of person or group.
-
E.
wornOver
Indicates that one item of clothing or accessory is positioned on top of and covering another item when worn.
- 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_69efb5207eb08190827e4c34048030b1 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f643f68c2c8190b44dd5a13238288a |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641e0fde08190bf06a1c5b388aa84 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 11:09 p.m.