Triple
T1379743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European Cup Winners’ Cup |
E29309
|
entity |
| Predicate | numberOfTitlesOfDynamoKyiv |
P25122
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [European Cup Winners’ Cup, numberOfTitlesOfDynamoKyiv, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTitlesOfDynamoKyiv Context triple: [European Cup Winners’ Cup, numberOfTitlesOfDynamoKyiv, 2]
-
A.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
-
B.
mastersTitles
Indicates that one entity holds one or more master's degree titles associated with another entity (such as an institution, field, or program).
-
C.
winnerTitleCount
chosen
Indicates the number of titles or championships an entity has won.
-
D.
numberOfSerieATitles
Indicates the number of Serie A championship titles that an entity has won.
-
E.
mostDivisionTitlesTeam
Indicates that the subject team holds the record for having won the greatest number of division titles compared to all other teams.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c319f46481909ba8a69a19b865e5 |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befcabdc8190a9f05d002603f81c |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.