Triple
T27229818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denys Puech |
E682122
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object |
Musée Denys-Puech
Musée Denys-Puech is an art museum in Rodez, France, known for its collection of sculptures and artworks, particularly those by its founder, the sculptor Denys Puech.
|
E1762515
|
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: Musée Denys-Puech | Statement: [Denys Puech, founded, Musée Denys-Puech]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Musée Denys-Puech Triple: [Denys Puech, founded, Musée Denys-Puech]
Generated description
Musée Denys-Puech is an art museum in Rodez, France, known for its collection of sculptures and artworks, particularly those by its founder, the sculptor Denys Puech.
Provenance (5 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_69eefacdad7881908b7bca61c90a1a1e |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6264dfc888190b2e25b84e8232246 |
completed | May 2, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12626e884c819092986b7623d9ac27 |
completed | May 24, 2026, 2:29 a.m. |
| NEDg | Description generation | batch_6a1263a80d848190ac06c46e255e9b26 |
completed | May 24, 2026, 2:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a126448a36c8190837c7ea378f68cd3 |
completed | May 24, 2026, 2:36 a.m. |
Created at: April 27, 2026, 9:45 a.m.