Triple
T7326250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Billericay |
E168881
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object |
Chauvigny
Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
|
E683119
|
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: Chauvigny | Statement: [Billericay, twinTown, Chauvigny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chauvigny Context triple: [Billericay, twinTown, Chauvigny]
-
A.
Verrières
Verrières is a small French commune located within the Thiers arrondissement in the Puy-de-Dôme department of central France.
-
B.
Viry-Châtillon
Viry-Châtillon is a suburban commune in the southern outskirts of Paris, France, known for its residential character and location along the Seine River in the Essonne department.
-
C.
Potigny
Potigny is a commune in the Calvados department of the Normandy region in northwestern France.
-
D.
Souvigny
Souvigny is a historic town in central France known for its important Cluniac priory and medieval religious heritage.
-
E.
Eygalières
Eygalières is a picturesque Provençal village in southern France, known for its stone houses, historic charm, and scenic setting amid the Alpilles hills.
- 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: Chauvigny Triple: [Billericay, twinTown, Chauvigny]
Generated description
Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chauvigny Target entity description: Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
-
A.
Verrières
Verrières is a small French commune located within the Thiers arrondissement in the Puy-de-Dôme department of central France.
-
B.
Viry-Châtillon
Viry-Châtillon is a suburban commune in the southern outskirts of Paris, France, known for its residential character and location along the Seine River in the Essonne department.
-
C.
Potigny
Potigny is a commune in the Calvados department of the Normandy region in northwestern France.
-
D.
Souvigny
Souvigny is a historic town in central France known for its important Cluniac priory and medieval religious heritage.
-
E.
Eygalières
Eygalières is a picturesque Provençal village in southern France, known for its stone houses, historic charm, and scenic setting amid the Alpilles hills.
- 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_69c68a54cacc81908e3b773441f19566 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f0a612c08190b7a3fefa811bbcec |
completed | March 27, 2026, 9:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8ac7fdaec8190a014513b8b60977b |
completed | March 29, 2026, 4:37 a.m. |
| NEDg | Description generation | batch_69c8aea3c1448190b254ce8be91e7806 |
completed | March 29, 2026, 4:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8af00d8e08190b9898cdeaeb611ae |
completed | March 29, 2026, 4:48 a.m. |
Created at: March 27, 2026, 3:03 p.m.