Triple
T21549030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albolote |
E531710
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object |
Maracena
Maracena is a municipality in the province of Granada, in the autonomous community of Andalusia in southern Spain.
|
E1498406
|
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: Maracena | Statement: [Albolote, hasNeighbouringMunicipality, Maracena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maracena Context triple: [Albolote, hasNeighbouringMunicipality, Maracena]
-
A.
Málaga
Málaga is a historic port city on Spain’s Costa del Sol, renowned for its Mediterranean beaches, rich Andalusian culture, and as the birthplace of artist Pablo Picasso.
-
B.
Malaga
Malaga is a white wine grape variety name historically used as a synonym for Sémillon in certain wine-growing regions.
-
C.
Antequera
Antequera is a historic city in Andalusia, southern Spain, known for its well-preserved medieval architecture and nearby prehistoric dolmens, a UNESCO World Heritage Site.
-
D.
Jaén
Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
-
E.
Jaén
Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
- 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: Maracena Triple: [Albolote, hasNeighbouringMunicipality, Maracena]
Generated description
Maracena is a municipality in the province of Granada, in the autonomous community of Andalusia in southern Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maracena Target entity description: Maracena is a municipality in the province of Granada, in the autonomous community of Andalusia in southern Spain.
-
A.
Málaga
Málaga is a historic port city on Spain’s Costa del Sol, renowned for its Mediterranean beaches, rich Andalusian culture, and as the birthplace of artist Pablo Picasso.
-
B.
Malaga
Malaga is a white wine grape variety name historically used as a synonym for Sémillon in certain wine-growing regions.
-
C.
Antequera
Antequera is a historic city in Andalusia, southern Spain, known for its well-preserved medieval architecture and nearby prehistoric dolmens, a UNESCO World Heritage Site.
-
D.
Jaén
Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
-
E.
Jaén
Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
- 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_69e0c45f17148190949c330ab9c27706 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeb590bb0881908cd849096696db10 |
completed | April 27, 2026, 1:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a249aea108190b9954345bab537d6 |
completed | May 17, 2026, 8:27 p.m. |
| NEDg | Description generation | batch_6a0a277b5c2c8190a1f27decb7cedf58 |
completed | May 17, 2026, 8:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a2802f0d481908cb62d5b95098d3b |
completed | May 17, 2026, 8:41 p.m. |
Created at: April 16, 2026, 6:28 p.m.