Triple
T9431112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Mancha |
E227375
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Consuegra
Consuegra is a historic town in Spain’s La Mancha region, known for its hilltop castle and iconic windmills associated with Don Quixote.
|
E801651
|
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: Consuegra | Statement: [La Mancha, hasTown, Consuegra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Consuegra Context triple: [La Mancha, hasTown, Consuegra]
-
A.
Churriana
Churriana is a district of Málaga in southern Spain, known for encompassing the area around Málaga–Costa del Sol Airport and lying close to the Mediterranean coast.
-
B.
Coveñas
Coveñas is a coastal municipality and popular beach destination on Colombia’s Caribbean Sea, known for its tourism and oil-related port activities.
-
C.
Requena
Requena is a small Peruvian city in the Loreto region, known as a remote Amazonian river port and gateway to surrounding rainforest communities.
-
D.
Requena
Requena is a historic inland town in Spain’s Valencian Community, known for its wine production and well-preserved medieval quarter.
-
E.
Aguadas
Aguadas is a historic Colombian town in the Caldas Department, known for its coffee culture, traditional hat-making, and well-preserved colonial 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: Consuegra Triple: [La Mancha, hasTown, Consuegra]
Generated description
Consuegra is a historic town in Spain’s La Mancha region, known for its hilltop castle and iconic windmills associated with Don Quixote.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Consuegra Target entity description: Consuegra is a historic town in Spain’s La Mancha region, known for its hilltop castle and iconic windmills associated with Don Quixote.
-
A.
Churriana
Churriana is a district of Málaga in southern Spain, known for encompassing the area around Málaga–Costa del Sol Airport and lying close to the Mediterranean coast.
-
B.
Coveñas
Coveñas is a coastal municipality and popular beach destination on Colombia’s Caribbean Sea, known for its tourism and oil-related port activities.
-
C.
Requena
Requena is a small Peruvian city in the Loreto region, known as a remote Amazonian river port and gateway to surrounding rainforest communities.
-
D.
Requena
Requena is a historic inland town in Spain’s Valencian Community, known for its wine production and well-preserved medieval quarter.
-
E.
Aguadas
Aguadas is a historic Colombian town in the Caldas Department, known for its coffee culture, traditional hat-making, and well-preserved colonial 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e5ed7408190beda5fb078e9345a |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12cd623308190ba55223399ee3aa8 |
completed | April 4, 2026, 3:23 p.m. |
| NEDg | Description generation | batch_69d12d8826248190aff8bf1ca1c15a03 |
completed | April 4, 2026, 3:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d12ddbfe988190b639bd16be05f343 |
completed | April 4, 2026, 3:27 p.m. |
Created at: March 30, 2026, 7:49 p.m.