Triple
T16883932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herman Van Springel |
E421489
|
entity |
| Predicate | numberOfBordeauxParisWins |
P124840
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Herman Van Springel, numberOfBordeauxParisWins, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBordeauxParisWins Context triple: [Herman Van Springel, numberOfBordeauxParisWins, 7]
-
A.
numberOfCoupeDeFranceTitles
Indicates the total count of Coupe de France titles that an entity has won.
-
B.
numberOfParisRoubaixWins
Indicates the count of times an entity has won the Paris–Roubaix cycling race.
-
C.
numberOfCoupeDeLaLigueTitles
Indicates the total count of Coupe de la Ligue titles that an entity has won.
-
D.
FrenchLeagueTitlesWith
Indicates a relationship where two entities are associated through having won French football league titles together (e.g., a club and a player sharing those titles).
-
E.
hasWonTopFlightLeagueInFrance
Indicates that the subject has won the championship title of the highest professional football league in France at least once.
- 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_69d889d470fc8190b4aec199636c0c56 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3bbbf0cec819084216807601afad1 |
completed | April 18, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69e32b90ec3c819099c51bb7baf2984c |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e32e2c07b081908c8fee9f5507bb9e |
completed | April 18, 2026, 7:09 a.m. |
Created at: April 10, 2026, 5:29 a.m.