Triple
T29710386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Germany vs Mexico |
E751761
|
entity |
| Predicate | GermanyPreviousWorldCupTitle |
P167808
|
FINISHED |
| Object | 2014 FIFA World Cup |
—
|
NE NERFINISHED |
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 | Statement: [Germany vs Mexico, GermanyPreviousWorldCupTitle, 2014 FIFA World Cup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: GermanyPreviousWorldCupTitle Context triple: [Germany vs Mexico, GermanyPreviousWorldCupTitle, 2014 FIFA World Cup]
-
A.
GermanyWorldCupTitlesAfterMatch
Indicates the number of FIFA World Cup titles Germany has won after the completion of a given match.
-
B.
numberOfGermanChampionships
Indicates the count of German championship titles associated with a given entity.
-
C.
worldCupWon
Indicates that the subject has won the FIFA World Cup tournament.
-
D.
WorldCupWinner
Indicates that the subject is the team or individual that won a specified FIFA World Cup tournament.
-
E.
previousFranceWorldCupTitleYear
Indicates the year in which France most recently won the FIFA World Cup prior to a given reference point.
- 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_69f0d62748848190b030d0a703629a7d |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f672d8463481909f9144dbfa8be162 |
completed | May 2, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66c59de9881909ebbb7b0ae7ab495 |
completed | May 2, 2026, 9:27 p.m. |
Created at: April 28, 2026, 7:30 p.m.