Triple
T15339248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rouen metropolitan area |
E366746
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Bois-Guillaume |
E884436
|
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: Bois-Guillaume | Statement: [Rouen metropolitan area, hasPart, Bois-Guillaume]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bois-Guillaume Context triple: [Rouen metropolitan area, hasPart, Bois-Guillaume]
-
A.
Bois-Guillaume
chosen
Bois-Guillaume is a suburban commune located near Rouen in northern France, known for its residential character and proximity to the city.
-
B.
Gagnière
Gagnière is a French surname, likely of regional origin, associated with individuals such as Mahoudeau.
-
C.
Surpierre
Surpierre is a small municipality in the canton of Fribourg in western Switzerland.
-
D.
Ganthier
Ganthier is a commune in western Haiti known for its rural character and proximity to the capital, Port-au-Prince.
-
E.
Dugommier
Dugommier was a French Revolutionary general noted for his leadership in key campaigns such as the Siege of Toulon and the War of the Pyrenees.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e12eb7c8190944a260aa1aa9156 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01f2ee9c819080fce24ed13a07c7 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.