Triple
T1162403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Touraine |
E24522
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Amboise |
E135329
|
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: Amboise | Statement: [Touraine, containsCity, Amboise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amboise Context triple: [Touraine, containsCity, Amboise]
-
A.
Amboise
chosen
Amboise is a historic town in central France on the Loire River, known for its royal château and as the place where Leonardo da Vinci spent his final years.
-
B.
Blois
Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
-
C.
Chinon
Chinon is a renowned Loire Valley wine appellation in France, best known for its elegant, medium-bodied red wines primarily made from Cabernet Franc.
-
D.
Poissy
Poissy is a commune in the western suburbs of Paris, France, known for hosting Le Corbusier’s iconic modernist Villa Savoye.
-
E.
Châteauroux
Châteauroux is a city in central France that will host the shooting events for the 2024 Summer Olympics.
- 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_69a494060e148190abb42f971242c197 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcb2bb84819088bd94e91c10fb0c |
completed | March 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acacacf7c4819089708cf61b89903c |
completed | March 7, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:45 p.m.