Triple
T5304232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Arrows |
E120057
|
entity |
| Predicate | numberOfLeagueTitles |
P63945
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [New York Arrows, numberOfLeagueTitles, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLeagueTitles Context triple: [New York Arrows, numberOfLeagueTitles, 4]
-
A.
consecutiveLeagueTitles
Indicates that one entity has won league titles in successive seasons without interruption.
-
B.
team2LeagueTitlesContext
Indicates that the second team has won league titles within a specified contextual scope (such as a particular time period, competition, or condition).
-
C.
numberOfTropheeDesChampionsTitles
Indicates the number of Trophée des Champions titles that an entity has won.
-
D.
numberOfCoupeDeLaLigueTitles
Indicates the total count of Coupe de la Ligue titles that an entity has won.
-
E.
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.
- 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_69bd44704be88190acdb2ac481b0ff55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8e44e7c881909b241b2fec366038 |
completed | March 20, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69bd845097ac81909678624c4907fda4 |
completed | March 20, 2026, 5:30 p.m. |
| PDg | Predicate description generation | batch_69bd8e43c4c88190bb72b9bf56c99425 |
completed | March 20, 2026, 6:13 p.m. |
Created at: March 20, 2026, 1:53 p.m.