Triple
T35665972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Van Gundy |
E1030562
|
entity |
| Predicate | countryMostAssociatedWith |
P6725
|
FINISHED |
| Object | United States of America |
E14
|
NE 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: United States of America | Statement: [Van Gundy, countryMostAssociatedWith, United States of America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryMostAssociatedWith Context triple: [Van Gundy, countryMostAssociatedWith, United States of America]
-
A.
countryKnownFor
Indicates that a country is widely recognized or notable for a particular characteristic, product, activity, or feature.
-
B.
capitalCountry
Indicates that one place serves as the capital city of a given country.
-
C.
coveredCountry
Indicates that one entity includes or provides coverage for the territory or jurisdiction of a specified country.
-
D.
hasOneOfLargestPopulationsOf
Indicates that the subject possesses or contains among the highest numbers of members or individuals of the specified type compared to others in a given context.
-
E.
associatedCountryMostProminently
chosen
Indicates the country with which an entity is most strongly or prominently associated, relative to any other countries it may be linked to.
- F. None of above.
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_69f76e09f87881909c954aaac176c34f |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a01e993fec0819090cc397218515e88 |
completed | May 11, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a38726c71bc81909f77eae2d4ffa442 |
completed | June 21, 2026, 11:23 p.m. |
| PD | Predicate disambiguation | batch_6a01e57594188190a2354f48cf71b48c |
completed | May 11, 2026, 2:19 p.m. |
Created at: May 3, 2026, 4:05 p.m.