Triple
T10193432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gueugnon |
E238095
|
entity |
| Predicate | hasLocalSports |
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: [Gueugnon, hasLocalSports, football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalSports Context triple: [Gueugnon, hasLocalSports, football]
-
A.
hasLocalSport
chosen
Indicates that a place or community is associated with, supports, or regularly engages in a particular sport at the local level.
-
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.
hasLocalFootballGround
Indicates that a place or organization possesses or is associated with a nearby football ground used for local matches or activities.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc675008190b8248325f5a208bf |
completed | April 2, 2026, 4:17 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8477648190bc55c56aeec507d3 |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:13 p.m.