Triple
T37071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Kingdom |
E732
|
entity |
| Predicate | sportsOrigin |
P1015
|
FINISHED |
| Object | association 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: association football | Statement: [United Kingdom, sportsOrigin, association football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportsOrigin Context triple: [United Kingdom, sportsOrigin, association football]
-
A.
popularSport
Indicates that a sport is widely liked, followed, or played by many people within a certain group or region.
-
B.
sport
Indicates that an entity participates in, is associated with, or is characterized by a particular athletic activity or game.
-
C.
sportCategory
Indicates that one entity is classified as a type or category of sport to which the other entity (typically a specific sport or sporting event) belongs.
-
D.
sportsRegionOf
chosen
Indicates that a region is the geographic or administrative area associated with a particular sports entity or activity.
-
E.
primarySport
Indicates the main sport with which an entity (such as a person, team, or organization) is most closely associated or primarily involved.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24bb753f081909cd8b25cfb8e08af |
completed | Feb. 28, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69a24ab4a6908190b6f355415ffe7948 |
completed | Feb. 28, 2026, 1:53 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.