Triple
T1030240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hueytown, Alabama |
E22232
|
entity |
| Predicate | hasSportsCulture |
P15165
|
FINISHED |
| Object | auto racing |
—
|
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: auto racing | Statement: [Hueytown, Alabama, hasSportsCulture, auto racing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSportsCulture Context triple: [Hueytown, Alabama, hasSportsCulture, auto racing]
-
A.
includesSport
chosen
Indicates that one entity contains, offers, or features a particular sport as part of its activities, content, or composition.
-
B.
popularSport
Indicates that a sport is widely liked, followed, or played by many people within a certain group or region.
-
C.
hasEsport
Indicates that an entity is associated with or participates in a specific electronic sports (esports) activity or competition.
-
D.
hasSportsStatus
Indicates that an entity holds a particular sports-related status, role, or classification (such as amateur, professional, active, or retired) within a sporting context.
-
E.
sportsInvolvement
Indicates the nature or extent of an entity’s participation in, association with, or role within a sport or sporting activity.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b95d35888190a20593a278175df7 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b7276180819085c6b23501a6a6e0 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.