Triple
T3768198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Auto |
E82730
|
entity |
| Predicate | competitionWith |
P1375
|
FINISHED |
| Object | Le Vélo |
E386112
|
NE 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: Le Vélo | Statement: [L’Auto, competitionWith, Le Vélo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Vélo Context triple: [L’Auto, competitionWith, Le Vélo]
-
A.
Le Vélo
chosen
Le Vélo was a pioneering French sports newspaper, particularly known for its coverage of cycling at the end of the 19th century.
-
B.
La Bicicleta
"La Bicicleta" is a popular Latin pop and vallenato fusion song by Colombian artists Shakira and Carlos Vives that celebrates Colombian culture and landscapes.
-
C.
La Tournette
La Tournette is a prominent peak in the French Alps known for its panoramic views over Lake Annecy and popular hiking routes.
-
D.
La Promenade
La Promenade is an Impressionist painting by Claude Monet depicting a woman with a parasol standing in a breezy, sunlit landscape.
-
E.
Le Reculet
Le Reculet is one of the highest summits in eastern France, known for its panoramic views over the surrounding Jura range and the Alps.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad8b207b0081909d2b48843fbd8795 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc2bdf6c819088d3c6ace83ca5ea |
completed | March 8, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb12d1688190b291bca5502c7174 |
completed | March 14, 2026, 6:07 a.m. |
Created at: March 8, 2026, 3:35 p.m.