Triple
T11734452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Ávila |
E278988
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Arévalo
Arévalo is a historic town in Spain’s Castile and León region, known for its well-preserved medieval architecture and Mudejar-style monuments.
|
E944176
|
NE FINISHED |
How this triple was built (4 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: Arévalo | Statement: [Province of Ávila, hasCity, Arévalo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arévalo Context triple: [Province of Ávila, hasCity, Arévalo]
-
A.
Almendralejo
Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
-
B.
Avilés
Avilés is a historic coastal city in northern Spain’s Asturias region, known for its medieval old town and long maritime and industrial heritage.
-
C.
Brihuega
Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
-
D.
Alcanena
Alcanena is a Portuguese municipality known for its traditional leather and tanning industry, located in the Centro Region of Portugal.
-
E.
Cabrils
Cabrils is a small municipality in the Maresme comarca of Catalonia, Spain, known for its residential character and proximity to the Mediterranean coast.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Arévalo Triple: [Province of Ávila, hasCity, Arévalo]
Generated description
Arévalo is a historic town in Spain’s Castile and León region, known for its well-preserved medieval architecture and Mudejar-style monuments.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arévalo Target entity description: Arévalo is a historic town in Spain’s Castile and León region, known for its well-preserved medieval architecture and Mudejar-style monuments.
-
A.
Almendralejo
Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
-
B.
Avilés
Avilés is a historic coastal city in northern Spain’s Asturias region, known for its medieval old town and long maritime and industrial heritage.
-
C.
Brihuega
Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
-
D.
Alcanena
Alcanena is a Portuguese municipality known for its traditional leather and tanning industry, located in the Centro Region of Portugal.
-
E.
Cabrils
Cabrils is a small municipality in the Maresme comarca of Catalonia, Spain, known for its residential character and proximity to the Mediterranean coast.
- F. None of above. chosen
Provenance (5 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4daa7f48190896fc7653e9dd70b |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f0199f595081908c10ecd7dd3900e7 |
completed | April 28, 2026, 2:21 a.m. |
| NEDg | Description generation | batch_69f01d7ab930819095eaae226ab55b80 |
completed | April 28, 2026, 2:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f043ddbfe481908e0c439dbd3e944f |
completed | April 28, 2026, 5:21 a.m. |
Created at: April 8, 2026, 9:41 p.m.