Triple
T36679460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British and Irish Lions tour to South Africa 2009 |
E905636
|
entity |
| Predicate | lionsTopPointsScorer |
P6605
|
FINISHED |
| Object | Stephen Jones |
—
|
NE NERFINISHED |
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: Stephen Jones | Statement: [British and Irish Lions tour to South Africa 2009, lionsTopPointsScorer, Stephen Jones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lionsTopPointsScorer Context triple: [British and Irish Lions tour to South Africa 2009, lionsTopPointsScorer, Stephen Jones]
-
A.
LionsTopTryScorer
Indicates that the entity is the player who scored the most tries for the Lions in a given context or competition.
-
B.
topScorer
chosen
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
C.
topScorerPoints
Indicates the number of points scored by the top-scoring entity in a given context or event.
-
D.
seasonTopScorerTeam1
Indicates that the referenced entity is the team whose player was the top scorer for team 1 in a given season.
-
E.
leagueTopScorerTeam
Indicates the team for which a given league’s top-scoring player plays or played.
- 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_69f76e7011dc819082b324f18b756a1b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c7be0be88190a480393a2d1ee5bf |
completed | May 3, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69f7c4796ebc819084a0dc08505e5f14 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:12 p.m.