Triple
T23064630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guy Ourisson |
E574997
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Ourisson
Ourisson is a French surname most notably associated with Guy Ourisson, a prominent chemist and academic.
|
E1571879
|
NE FINISHED |
How this triple was built (4 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: Ourisson | Statement: [Guy Ourisson, familyName, Ourisson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ourisson Context triple: [Guy Ourisson, familyName, Ourisson]
-
A.
Saussignac
Saussignac is a small wine-producing commune in southwestern France, known for its sweet white wines made primarily from Sémillon and other Bordeaux grape varieties.
-
B.
Rothière
Rothière is a small commune in the Aube department of north-central France, situated within the Grand Est region.
-
C.
Gressy
Gressy is a small French commune located in the Île-de-France region, known for its residential character and proximity to Paris and Charles de Gaulle Airport.
-
D.
Mouriès
Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
-
E.
Malbuisson
Malbuisson is a lakeside commune in the Doubs department of eastern France, known for its scenic setting on the shores of Lake Saint-Point and outdoor recreational activities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Ourisson Triple: [Guy Ourisson, familyName, Ourisson]
Generated description
Ourisson is a French surname most notably associated with Guy Ourisson, a prominent chemist and academic.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ourisson Target entity description: Ourisson is a French surname most notably associated with Guy Ourisson, a prominent chemist and academic.
-
A.
Saussignac
Saussignac is a small wine-producing commune in southwestern France, known for its sweet white wines made primarily from Sémillon and other Bordeaux grape varieties.
-
B.
Rothière
Rothière is a small commune in the Aube department of north-central France, situated within the Grand Est region.
-
C.
Gressy
Gressy is a small French commune located in the Île-de-France region, known for its residential character and proximity to Paris and Charles de Gaulle Airport.
-
D.
Mouriès
Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
-
E.
Malbuisson
Malbuisson is a lakeside commune in the Doubs department of eastern France, known for its scenic setting on the shores of Lake Saint-Point and outdoor recreational activities.
- F. None of above. chosen
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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a2eb5c81908a90e22ff2a56430 |
completed | April 29, 2026, 4:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c23da2dfc8190a8b9239fa1ad5ebe |
completed | May 19, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_6a0c271d8fc48190a818c73660218022 |
completed | May 19, 2026, 9:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c2951a718819088c1c7d8435586ec |
completed | May 19, 2026, 9:11 a.m. |
Created at: April 17, 2026, 3:55 p.m.