Triple
T21741148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Passarella |
E536660
|
entity |
| Predicate | goalscoringDefender |
P140453
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Daniel Passarella, goalscoringDefender, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalscoringDefender Context triple: [Daniel Passarella, goalscoringDefender, yes]
-
A.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
-
B.
famousGoalAgainst
Indicates that one entity is widely recognized for scoring a notable or iconic goal against another entity.
-
C.
isProlificGoalScorer
Indicates that an entity consistently scores a high number of goals, marking them as notably productive or effective in goal scoring.
-
D.
notableGoalScorer
chosen
Indicates that the subject is recognized for having scored a significant or noteworthy number of goals, typically in a sports context.
-
E.
topGoalScorerGoals
Indicates the number of goals scored by the top goal scorer in a given context or competition.
- 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a7250d48190aa63f89db017ef70 |
completed | April 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69e6969c16fc8190b5126c169317d85d |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:49 p.m.