Triple
T36402907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FA Cup with Manchester United 1962–63 |
E896677
|
entity |
| Predicate | goalsByOpponent |
P185736
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [FA Cup with Manchester United 1962–63, goalsByOpponent, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalsByOpponent Context triple: [FA Cup with Manchester United 1962–63, goalsByOpponent, 1]
-
A.
mostGoalsInSingleGameOpponent
Indicates the opposing team against which an entity scored its highest number of goals in a single game.
-
B.
famousGoalAgainst
Indicates that one entity is widely recognized for scoring a notable or iconic goal against another entity.
-
C.
recordOpponent
Indicates that one entity is documented or registered as the opposing party or competitor of another entity.
-
D.
leagueGoalsAgainst
Indicates the number of goals a team has conceded in league competition against its opponents.
-
E.
goalsConceded
Indicates the number of goals a team or player has allowed the opposing side to score.
- 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_69f76e53b81081908d3b81860593f38a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
| PDg | Predicate description generation | batch_69f7c3705b5c81908c84004543a71c07 |
completed | May 3, 2026, 9:51 p.m. |
Created at: May 3, 2026, 4:10 p.m.