Triple
T5544853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerd Müller |
E145380
|
entity |
| Predicate | goalsScoredForClub |
P9098
|
FINISHED |
| Object | Bayern Munich: 398 goals in 453 league appearances |
—
|
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: Bayern Munich: 398 goals in 453 league appearances | Statement: [Gerd Müller, goalsScoredForClub, Bayern Munich: 398 goals in 453 league appearances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsScoredForClub Context triple: [Gerd Müller, goalsScoredForClub, Bayern Munich: 398 goals in 453 league appearances]
-
A.
goalScorerTeam
Indicates that a team is the one for which a particular goal scorer scored a goal.
-
B.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
C.
scoredFor
Indicates that one entity achieved points or a score on behalf of another entity, such as a player scoring for a team.
-
D.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
E.
clubAppearances
Indicates the number of official matches a player has played for a particular club.
- 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_69c008fb879c81909f5bfa56fadc1d46 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01fcad7d88190b83bb4ecb3b34bfd |
completed | March 22, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69c01b0e72f08190bf705d8fe1639401 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:35 p.m.