Triple
T18964615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nantes metropolitan area |
E464001
|
entity |
| Predicate | hasSuburb |
P747
|
FINISHED |
| Object | Carquefou |
—
|
NE NERFINISHED |
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: Carquefou | Statement: [Nantes metropolitan area, hasSuburb, Carquefou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carquefou Context triple: [Nantes metropolitan area, hasSuburb, Carquefou]
-
A.
Carquefou
chosen
Carquefou is a commune in western France, situated near Nantes and known for its residential character and economic activity.
-
B.
Fourqueux
Fourqueux is a suburban commune in the Yvelines department of north-central France, located to the west of Paris.
-
C.
Casseneuil
Casseneuil is a small commune in southwestern France, located in the Lot-et-Garonne department in the Nouvelle-Aquitaine region.
-
D.
Assencières
Assencières is a small commune in the Aube department of north-central France.
-
E.
Aiguillon
Aiguillon is a commune in southwestern France, known for its strategic location at the confluence of the Lot and Garonne rivers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d5365881909dcbb4988d273b24 |
completed | April 20, 2026, 7:29 a.m. |
Created at: April 10, 2026, noon