Triple
T14695533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Ciudad Real |
E345146
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Puertollano
Puertollano is an industrial city in central Spain known for its historical coal mining and energy production industries.
|
E1153966
|
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: Puertollano | Statement: [Province of Ciudad Real, hasMunicipality, Puertollano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Puertollano Context triple: [Province of Ciudad Real, hasMunicipality, Puertollano]
-
A.
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.
-
B.
Yecla
Yecla is a Spanish wine region in the province of Murcia, particularly noted for its robust red wines made predominantly from the Mourvèdre (Monastrell) grape.
-
C.
Tarancón
Tarancón is a historic market town and important transport hub in central Spain’s Castilla-La Mancha region.
-
D.
Almendralejo
Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
-
E.
Utrera
Utrera is a historic town in southern Spain’s Andalusia region, known for its rich flamenco heritage, traditional bullfighting culture, and well-preserved architecture.
- 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: Puertollano Triple: [Province of Ciudad Real, hasMunicipality, Puertollano]
Generated description
Puertollano is an industrial city in central Spain known for its historical coal mining and energy production industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Puertollano Target entity description: Puertollano is an industrial city in central Spain known for its historical coal mining and energy production industries.
-
A.
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.
-
B.
Yecla
Yecla is a Spanish wine region in the province of Murcia, particularly noted for its robust red wines made predominantly from the Mourvèdre (Monastrell) grape.
-
C.
Tarancón
Tarancón is a historic market town and important transport hub in central Spain’s Castilla-La Mancha region.
-
D.
Almendralejo
Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
-
E.
Utrera
Utrera is a historic town in southern Spain’s Andalusia region, known for its rich flamenco heritage, traditional bullfighting culture, and well-preserved architecture.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb58855e081908b38f9515db5677f |
completed | April 14, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13322c548190bac21db2bfa56ee9 |
completed | May 9, 2026, 10:57 a.m. |
| NEDg | Description generation | batch_69ff14042ce8819084817836b096f175 |
completed | May 9, 2026, 11:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff14745a8c81909b10d6b21b88b50b |
completed | May 9, 2026, 11:03 a.m. |
Created at: April 10, 2026, 1:28 a.m.