Triple
T28936560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raúl Tamudo |
E730331
|
entity |
| Predicate | numberOfGoalsForEspanyol |
P200803
|
FINISHED |
| Object | over 100 league goals |
—
|
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: over 100 league goals | Statement: [Raúl Tamudo, numberOfGoalsForEspanyol, over 100 league goals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGoalsForEspanyol Context triple: [Raúl Tamudo, numberOfGoalsForEspanyol, over 100 league goals]
-
A.
equalisingGoalScorerForSpain
Indicates that the referenced entity is the player who scored the goal that brought Spain’s score level with their opponent.
-
B.
secondSpainGoalScoredBy
Indicates the relationship where a specific entity is the player who scored the second goal for Spain in a given match or event.
-
C.
clubNumberOfGoalsForFCBarcelona
Indicates the number of goals an entity has scored while playing for FC Barcelona at club level.
-
D.
clubNumberAtEspanyol
Indicates the squad number a player wears or wore while playing for the club Espanyol.
-
E.
goalsByRealMadrid
Indicates the number of goals that were scored by Real Madrid.
- 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_69f043ea0aa88190a25acbf46157995a |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69ffada24d188190a576a02dc280a7fb |
completed | May 9, 2026, 9:56 p.m. |
| PD | Predicate disambiguation | batch_69ffad46d6ac819081772f408b1389d5 |
completed | May 9, 2026, 9:55 p.m. |
| PDg | Predicate description generation | batch_69ffada149748190916bfd9b87356342 |
completed | May 9, 2026, 9:56 p.m. |
Created at: April 28, 2026, 8:32 a.m.