Triple
T31357315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bon Accord F.C. |
E799770
|
entity |
| Predicate | goalsScoredInRecordMatch |
P175468
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [Bon Accord F.C., goalsScoredInRecordMatch, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsScoredInRecordMatch Context triple: [Bon Accord F.C., goalsScoredInRecordMatch, 0]
-
A.
numberOfGoalsInRecordTournament
Indicates the total count of goals an entity scored in a specific record-setting tournament.
-
B.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
C.
recordGoalsHolder
Indicates that the subject entity holds the record for the highest number of goals scored in a given context.
-
D.
mostGoalsInSingleGamePlayer
Indicates the player who scored the highest number of goals in a single game.
-
E.
recordHighScoringForWinner
Indicates that a record is kept of the highest score achieved by the winning entity in a given context or event.
- 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_69f224e5e9bc8190a16339328897c4f8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6d210fc80819091ed8961aa2cddfb |
completed | May 3, 2026, 4:41 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d16b79dc8190ab0d4657f2ef9a5b |
completed | May 3, 2026, 4:39 a.m. |
Created at: April 29, 2026, 9:17 p.m.