Triple
T9302630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English First Division 1952–53 |
E223802
|
entity |
| Predicate | runnersUpGoalAverage |
P87966
|
FINISHED |
| Object | 1.417 |
—
|
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: 1.417 | Statement: [English First Division 1952–53, runnersUpGoalAverage, 1.417]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runnersUpGoalAverage Context triple: [English First Division 1952–53, runnersUpGoalAverage, 1.417]
-
A.
runnerUpScore
Indicates the score achieved by the participant or entity that finished in second place in a competition or ranking.
-
B.
pslRunnersUp
Indicates that the subject finished in second place (as a runner-up) in the PSL competition or event specified by the object.
-
C.
worldCupRunnersUpFinishes
Indicates the number of times an entity has finished as the runner-up in the FIFA World Cup.
-
D.
averageGoalsPerMatch
Indicates the typical number of goals scored per match in the context of the given entities or competition.
-
E.
gamesWonByRunnerUp
Indicates the number of games won by the runner-up in a competition or match.
- 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_69ca8424d0f08190831e2e93c6533aeb |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd08d34c4c819095a213360747c3a6 |
completed | April 1, 2026, noon |
| PD | Predicate disambiguation | batch_69cc7a5ef1908190bc5ca166bb895af6 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc955a38108190b602d1e73725f11b |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:36 p.m.