Triple
T12490521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UEFA Champions League 2011–12 with Chelsea |
E298548
|
entity |
| Predicate | aggregateScoreVsBenfica |
P105271
|
FINISHED |
| Object | 3–1 |
—
|
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: 3–1 | Statement: [UEFA Champions League 2011–12 with Chelsea, aggregateScoreVsBenfica, 3–1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aggregateScoreVsBenfica Context triple: [UEFA Champions League 2011–12 with Chelsea, aggregateScoreVsBenfica, 3–1]
-
A.
scoreJuventus
Indicates that an entity scores a goal for the Juventus football team in a match.
-
B.
bestUEFACupPerformance
Indicates the highest level or furthest stage an entity has ever reached in UEFA Cup competition.
-
C.
goalsByRealMadrid
Indicates the number of goals that were scored by Real Madrid.
-
D.
leagueGoalDifference
Indicates the numerical difference between goals scored and goals conceded by an entity within a league competition.
-
E.
clubNumberOfGoalsForBudapestHonved
Indicates the number of goals scored for the club Budapest Honvéd by a given player.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e8a706c8190873623eab7db607d |
completed | April 10, 2026, 7:24 p.m. |
| PD | Predicate disambiguation | batch_69d94d41f3cc8190a3331fb9a895306f |
completed | April 10, 2026, 7:19 p.m. |
| PDg | Predicate description generation | batch_69d94e87e0e88190bc49dcfe4954c7e0 |
completed | April 10, 2026, 7:24 p.m. |
Created at: April 8, 2026, 9:56 p.m.