Triple
T18291047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagnes |
E438114
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Bruson |
—
|
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: Bruson | Statement: [Bagnes, contains, Bruson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bruson Context triple: [Bagnes, contains, Bruson]
-
A.
Bruson
chosen
Bruson is a quieter, traditional Swiss alpine village and ski area in the Valais region, known for its tree-lined slopes and access to the larger 4 Vallées domain.
-
B.
Balzar
Balzar is a town and agricultural center in coastal Ecuador, known for its rice and banana production within Guayas Province.
-
C.
Bruschi
Bruschi is the surname of Tedy Bruschi, a former NFL linebacker best known for his career with the New England Patriots.
-
D.
Boulgher
Boulgher is an alternative spelling or variant form of the surname Bulger.
-
E.
Domaso
Domaso is a picturesque lakeside town on the northern shore of Lake Como in Italy, known for its waterfront promenades, water sports, and scenic mountain backdrop.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500fee5248190928e68ddaa4d90d7 |
completed | April 19, 2026, 4:21 p.m. |
Created at: April 10, 2026, 10:35 a.m.