Triple
T5331684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orne |
E123324
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Alençon |
E348749
|
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: Alençon | Statement: [Orne, capital, Alençon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alençon Context triple: [Orne, capital, Alençon]
-
A.
Alençon
chosen
Alençon is a historic town in northwestern France renowned for its fine lace-making tradition and architectural heritage.
-
B.
Chapeauroux
Chapeauroux is a river in central France that flows through the Massif Central before joining the Allier.
-
C.
Angers
Angers is a historic city in western France known for its medieval architecture, including the Château d'Angers and its famous Apocalypse Tapestry.
-
D.
Châteauroux
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
-
E.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
- 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_69bd46477f9081909d242a327d749466 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85aab0308190990626cbc9da3e21 |
completed | March 20, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf9f5885388190bf06b919425a81fe |
completed | March 22, 2026, 7:50 a.m. |
Created at: March 20, 2026, 2 p.m.