Triple
T7069289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aenor de Châtellerault |
E164643
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object |
Talmont
Talmont is a historic coastal village in southwestern France, known for its medieval architecture and scenic position overlooking the Gironde estuary.
|
E640360
|
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: Talmont | Statement: [Aenor de Châtellerault, placeOfDeath, Talmont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Talmont Context triple: [Aenor de Châtellerault, placeOfDeath, Talmont]
-
A.
Sarratt
Sarratt is a rural village in Hertfordshire, England, known for its traditional village green, historic buildings, and scenic Chilterns countryside.
-
B.
Tilghman
Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
-
C.
Yatesville
Yatesville is a small town located in the U.S. state of Georgia.
-
D.
Whitehill
Whitehill is a small town and civil parish in East Hampshire, England, known for its proximity to the former army town of Bordon and the surrounding heathland and woodland.
-
E.
Luthersville
Luthersville is a small rural city in Meriwether County, Georgia, known for its quiet residential character and location in west-central Georgia.
- 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: Talmont Triple: [Aenor de Châtellerault, placeOfDeath, Talmont]
Generated description
Talmont is a historic coastal village in southwestern France, known for its medieval architecture and scenic position overlooking the Gironde estuary.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Talmont Target entity description: Talmont is a historic coastal village in southwestern France, known for its medieval architecture and scenic position overlooking the Gironde estuary.
-
A.
Sarratt
Sarratt is a rural village in Hertfordshire, England, known for its traditional village green, historic buildings, and scenic Chilterns countryside.
-
B.
Tilghman
Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
-
C.
Yatesville
Yatesville is a small town located in the U.S. state of Georgia.
-
D.
Whitehill
Whitehill is a small town and civil parish in East Hampshire, England, known for its proximity to the former army town of Bordon and the surrounding heathland and woodland.
-
E.
Luthersville
Luthersville is a small rural city in Meriwether County, Georgia, known for its quiet residential character and location in west-central Georgia.
- 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_69c6887b96548190a8a9b3ac8adf4119 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4aa82108190bacd5584c1c78999 |
completed | March 27, 2026, 8:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c794552c108190adfbbcae38c91a28 |
completed | March 28, 2026, 8:41 a.m. |
| NEDg | Description generation | batch_69c795143ad481909d0395ca41502e42 |
completed | March 28, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c795fce734819086e20a916aa67f54 |
completed | March 28, 2026, 8:49 a.m. |
Created at: March 27, 2026, 2:39 p.m.