Triple
T5000217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AO |
E112352
|
entity |
| Predicate | represents |
P129
|
FINISHED |
| Object | Aosta |
E110150
|
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: Aosta | Statement: [AO, represents, Aosta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aosta Context triple: [AO, represents, Aosta]
-
A.
Aosta
chosen
Aosta is a historic town in northwestern Italy known as the capital of the Aosta Valley region and for its well-preserved Roman and medieval architecture.
-
B.
Léognan
Léognan is a renowned wine-producing commune in southwestern France, celebrated for its prestigious red and white Bordeaux wines.
-
C.
Domodossola
Domodossola is a town in northern Italy’s Piedmont region, historically situated in the territory once inhabited by the ancient Lepontii people.
-
D.
Biella
Biella is a city in the Piedmont region of northern Italy, known for its textile industry and Alpine foothill setting.
-
E.
Baveno
Baveno is a picturesque lakeside town in northern Italy, known for its scenic views of Lake Maggiore and its historic villas and churches.
- 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd72bd90948190bf6ca21237402949 |
completed | March 20, 2026, 4:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a3a5a108190a028920b1ae0be7a |
completed | March 21, 2026, 12:08 p.m. |
Created at: March 20, 2026, 1:34 p.m.