Triple
T27932627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Benteke |
E708023
|
entity |
| Predicate | missedTournament |
P117392
|
FINISHED |
| Object | 2014 FIFA World Cup due to injury |
—
|
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: 2014 FIFA World Cup due to injury | Statement: [Christian Benteke, missedTournament, 2014 FIFA World Cup due to injury]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: missedTournament Context triple: [Christian Benteke, missedTournament, 2014 FIFA World Cup due to injury]
-
A.
didNotPlayInTournament
chosen
Indicates that the specified entity did not participate as a player in the referenced tournament.
-
B.
wonTournament
Indicates that an entity emerged as the overall victor in a tournament competition.
-
C.
otherTournament
Indicates a relationship where one tournament is distinct from and not the same as another tournament.
-
D.
tournamentsNotHeld
Indicates that certain tournaments did not take place or were not conducted as scheduled.
-
E.
scoredInTournament
Indicates that an entity achieved a score or points during a particular tournament.
- 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_69ef96bbf2c48190a9d0e0291457aab6 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f63b317e048190963989b732b25b91 |
completed | May 2, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69f6370ea79c81909b761821ee0fa698 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 7:03 p.m.