Triple
T6737064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honduras national football team |
E153981
|
entity |
| Predicate | regionalTitleCount |
P14832
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Honduras national football team, regionalTitleCount, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalTitleCount Context triple: [Honduras national football team, regionalTitleCount, 1]
-
A.
regionalTitles
chosen
Indicates that an entity holds or is associated with titles or designations that are specific to particular geographic regions.
-
B.
divisionTitleCount
Indicates the number of titles or championships associated with a particular division.
-
C.
subRegionalTitles
Indicates that an entity has one or more titles that apply specifically within a sub-region of a larger region or jurisdiction.
-
D.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
E.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
- 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_69c6880d84d8819095d19de2295f26ac |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1850a288190aa7e647fefbb0ede |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d09067a0819087ed6c820f4699f8 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:10 p.m.