Triple
T1270751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Zay |
E15702
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object |
Molles
Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
|
E144607
|
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: Molles | Statement: [Jean Zay, placeOfDeath, Molles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Molles Context triple: [Jean Zay, placeOfDeath, Molles]
-
A.
Guasca
Guasca is a small town and municipality in the Cundinamarca Department of Colombia, known for its rural landscapes and proximity to the Chingaza National Natural Park.
-
B.
Terra Alta
Terra Alta is a rural comarca in southern Catalonia, Spain, known for its wine production and as a major battleground during the Spanish Civil War.
-
C.
Ayguemarse
Ayguemarse is a smaller watercourse in southeastern France that serves as one of the contributing streams feeding the Ouvèze River.
-
D.
Cieneguilla
Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
-
E.
Cañete
Cañete is a coastal province and agricultural hub in central Peru, known for its fertile valleys, Afro-Peruvian cultural heritage, and production of crops like grapes and cotton.
- 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: Molles Triple: [Jean Zay, placeOfDeath, Molles]
Generated description
Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Molles Target entity description: Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
-
A.
Guasca
Guasca is a small town and municipality in the Cundinamarca Department of Colombia, known for its rural landscapes and proximity to the Chingaza National Natural Park.
-
B.
Terra Alta
Terra Alta is a rural comarca in southern Catalonia, Spain, known for its wine production and as a major battleground during the Spanish Civil War.
-
C.
Ayguemarse
Ayguemarse is a smaller watercourse in southeastern France that serves as one of the contributing streams feeding the Ouvèze River.
-
D.
Cieneguilla
Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
-
E.
Cañete
Cañete is a coastal province and agricultural hub in central Peru, known for its fertile valleys, Afro-Peruvian cultural heritage, and production of crops like grapes and cotton.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c0691d70819088e57c78ff34af1e |
completed | March 1, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac998b819c8190ad5a4095d31b5cb1 |
completed | March 7, 2026, 9:32 p.m. |
| NEDg | Description generation | batch_69ac9a13e9548190ae1fbfeba3326cd5 |
completed | March 7, 2026, 9:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac9a96d4f081908e608a3f247bbfb2 |
completed | March 7, 2026, 9:37 p.m. |
Created at: March 1, 2026, 7:50 p.m.