Triple
T3730159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arsène Wenger |
E79041
|
entity |
| Predicate | PremierLeagueTitlesWithArsenal |
P51299
|
FINISHED |
| Object | 3 |
—
|
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 | Statement: [Arsène Wenger, PremierLeagueTitlesWithArsenal, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: PremierLeagueTitlesWithArsenal Context triple: [Arsène Wenger, PremierLeagueTitlesWithArsenal, 3]
-
A.
numberOfTitlesOfChelsea
Indicates the total count of titles that Chelsea has won.
-
B.
LeagueCupTitles
Indicates the number of league cup championships an entity has won.
-
C.
PremierLeagueTitlesWithManchesterCityAsManager
Indicates the number of Premier League titles a manager has won while managing Manchester City.
-
D.
numberOfTitlesOfMostSuccessfulClub
Indicates the total count of titles won by the club that has achieved the highest number of titles among all clubs in the given context.
-
E.
consecutiveLeagueTitles
Indicates that one entity has won league titles in successive seasons without interruption.
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb1bb5408190990ea4dfbdab5c68 |
completed | March 8, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69adc0452f5081909c79e114a86cce8c |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc226fffc81909c679b44e611fee6 |
completed | March 8, 2026, 6:38 p.m. |
Created at: March 8, 2026, 3:34 p.m.