Triple
T13450182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brunete |
E320587
|
entity |
| Predicate | hasOfficialName |
P66
|
FINISHED |
| Object | Brunete |
E320587
|
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: Brunete | Statement: [Brunete, hasOfficialName, Brunete]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brunete Context triple: [Brunete, hasOfficialName, Brunete]
-
A.
Brunete
chosen
Brunete is a town in the Community of Madrid, Spain, historically notable as a major battleground of the Spanish Civil War.
-
B.
Ortigueira
Ortigueira is a coastal town and municipality in the province of A Coruña, Galicia, Spain, known for its scenic estuary landscapes and its international folk music festival.
-
C.
Risca
Risca is a town in south Wales situated in the county borough of Caerphilly, near Newport, with a history rooted in coal mining and industry.
-
D.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
-
E.
Barajas
Barajas is a Madrid Metro station on Line 8 that serves the Barajas district near Madrid–Barajas Airport.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaef973b08190a3d7fe1c2a913cff |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73999f8388190b2c578e063341178 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:41 p.m.