Triple
T6236416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beaumont-en-Auge |
E139488
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Lisieux |
E230690
|
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: Lisieux | Statement: [Beaumont-en-Auge, hasNearbyCity, Lisieux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisieux Context triple: [Beaumont-en-Auge, hasNearbyCity, Lisieux]
-
A.
Lisieux
chosen
Lisieux is a town and commune in the Calvados department of Normandy in northwestern France, known as a major Catholic pilgrimage site associated with Saint Thérèse of Lisieux.
-
B.
Serigny
Serigny is a French given name historically associated with the colonial-era figure Serigny Le Moyne.
-
C.
Cholet
Cholet is a town in western France’s Maine-et-Loire department, known historically for its textile industry and as part of the Pays de la Loire region.
-
D.
Saint-Brais
Saint-Brais is a small rural municipality in the canton of Jura in northwestern Switzerland, situated in the Franches-Montagnes district.
-
E.
Saint-Mard
Saint-Mard is a French commune in the Seine-et-Marne department in the Île-de-France region, northeast of Paris.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008b0e7ac8190808a59573ee646f3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063021258819093a9237041816638 |
completed | March 22, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640a59c70819093a67d1a16fcef8b |
completed | March 27, 2026, 8:32 a.m. |
Created at: March 22, 2026, 4:23 p.m.