Triple
T26681010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kurt Hamrin |
E672609
|
entity |
| Predicate | goalScoringRecord |
P103762
|
FINISHED |
| Object | one of Fiorentina’s all-time leading scorers |
—
|
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: one of Fiorentina’s all-time leading scorers | Statement: [Kurt Hamrin, goalScoringRecord, one of Fiorentina’s all-time leading scorers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalScoringRecord Context triple: [Kurt Hamrin, goalScoringRecord, one of Fiorentina’s all-time leading scorers]
-
A.
recordGoalsHolder
chosen
Indicates that the subject entity holds the record for the highest number of goals scored in a given context.
-
B.
topGoalScorerGoals
Indicates the number of goals scored by the top goal scorer in a given context or competition.
-
C.
fastestGoalScorer
Indicates that the subject is the player who scored a goal in the shortest time (e.g., from match start or appearance) compared to others.
-
D.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
E.
goalScorer
Indicates that the subject is the player who scored a particular goal in a game or match.
- 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_69eecda13424819092b17942c4edf722 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f61fd623bc819091df736cf3419b99 |
completed | May 2, 2026, 4:01 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 3:19 a.m.