Triple
T8881615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elvas |
E211423
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Badajoz |
E184815
|
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: Badajoz | Statement: [Elvas, nearbyCity, Badajoz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Badajoz Context triple: [Elvas, nearbyCity, Badajoz]
-
A.
Badajoz
chosen
Badajoz is a historic city in western Spain near the Portuguese border, known for its medieval fortress and role as a strategic frontier stronghold.
-
B.
Burgos
Burgos is a historic city in northern Spain known for its medieval architecture and its prominent role during the Spanish Civil War.
-
C.
Burgos
Burgos is a small coastal municipality on the northern tip of Siargao Island in the Philippines, known for its quiet beaches and surf spots.
-
D.
Béjar
Béjar is a historic town in the province of Salamanca, Spain, known for its textile heritage and scenic setting in the Sierra de Béjar mountains.
-
E.
Ávila
Ávila is a historic walled city in central Spain, renowned for its remarkably well-preserved medieval fortifications and Romanesque and Gothic architecture.
- 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6168e3d881908c58cf11cf5f9a0e |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b165fb0c81908c79b6ade3cca20e |
completed | April 4, 2026, 6:36 a.m. |
Created at: March 30, 2026, 6:53 p.m.