Triple
T31357314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bon Accord F.C. |
E799770
|
entity |
| Predicate | goalsConcededInRecordMatch |
P128971
|
FINISHED |
| Object | 36 |
—
|
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: 36 | Statement: [Bon Accord F.C., goalsConcededInRecordMatch, 36]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsConcededInRecordMatch Context triple: [Bon Accord F.C., goalsConcededInRecordMatch, 36]
-
A.
fewestGoalsConcededRecordScope
Indicates the specific context or scope (such as competition, season, or time period) within which a record for the fewest goals conceded is defined.
-
B.
goalsConceded
chosen
Indicates the number of goals a team or player has allowed the opposing side to score.
-
C.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
D.
numberOfGoalsInRecordTournament
Indicates the total count of goals an entity scored in a specific record-setting tournament.
-
E.
mostGoalsInSingleGameOpponent
Indicates the opposing team against which an entity scored its highest number of goals in a single game.
- 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_69f224e5e9bc8190a16339328897c4f8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6cd126fcc8190aa1f1f146e45ec0c |
completed | May 3, 2026, 4:20 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1470808190b70cdfd7a6395670 |
completed | May 3, 2026, 4:16 a.m. |
Created at: April 29, 2026, 9:17 p.m.