Triple
T11834380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Division 1972–73 |
E281476
|
entity |
| Predicate | championsGoalsAgainst |
P51307
|
FINISHED |
| Object | 42 |
—
|
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: 42 | Statement: [First Division 1972–73, championsGoalsAgainst, 42]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: championsGoalsAgainst Context triple: [First Division 1972–73, championsGoalsAgainst, 42]
-
A.
championsGoalAverage
Indicates the average number of goals scored (or conceded) by a champion over a defined set of games or season.
-
B.
leagueGoalsAgainst
chosen
Indicates the number of goals a team has conceded in league competition against its opponents.
-
C.
numberOfGoals
Indicates the total count of goals scored or achieved by an entity in a given context.
-
D.
mostGoalsInSingleGameOpponent
Indicates the opposing team against which an entity scored its highest number of goals in a single game.
-
E.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
- 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62e7e408190998bebe346c82e89 |
completed | April 10, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69d8a251fc08819095933f1d13c3b742 |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:43 p.m.