Triple
T10120227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Cup |
E223266
|
entity |
| Predicate | matchScoringSystem |
P79635
|
FINISHED |
| Object | best of three games to 21 points |
—
|
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: best of three games to 21 points | Statement: [Thomas Cup, matchScoringSystem, best of three games to 21 points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: matchScoringSystem Context triple: [Thomas Cup, matchScoringSystem, best of three games to 21 points]
-
A.
matchSystem
Indicates a relationship where an entity is paired or aligned with a particular system according to defined matching criteria.
-
B.
hasScoreSystem
chosen
Indicates that an entity uses, is governed by, or is associated with a particular scoring or rating system.
-
C.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
D.
scoring
Indicates the act of achieving points or a measurable result, typically by successfully completing an action that contributes to a score or outcome.
-
E.
introducedScoringSystem
Indicates that an entity initiated or brought into use a particular scoring system.
- 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_69ca8422047c81909d66b717b8b18cf3 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd2659cdc8190b3ba91426bda55ec |
completed | April 2, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba1d360819087698d04a53cc87e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:04 p.m.