Triple
T7618925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cleland |
E172435
|
entity |
| Predicate | hasLocalSport |
P78147
|
FINISHED |
| Object | football |
—
|
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: football | Statement: [Cleland, hasLocalSport, football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalSport Context triple: [Cleland, hasLocalSport, football]
-
A.
hasSportingEvent
Indicates that a sporting event is associated with, held at, or organized by a given entity.
-
B.
hasMajorSport
Indicates that an entity is associated with or participates in a primary or most prominent sport.
-
C.
hasSportsGround
Indicates that an entity possesses, includes, or is associated with a sports ground or athletic field as part of its facilities or area.
-
D.
hasSportsService
Indicates that an entity provides or is associated with a sports-related service.
-
E.
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.
- F. None of above. chosen
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_69c699506b308190826894dab1d9ea86 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fe73ff7c8190ab1218d97b37416d |
completed | March 27, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e725a88190b1f05dd224f7f4f2 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6fe7323b0819081664662d2f26937 |
completed | March 27, 2026, 10:02 p.m. |
Created at: March 27, 2026, 3:55 p.m.