Triple
T3099637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bailén |
E64682
|
entity |
| Predicate | isLocatedNear |
P350
|
FINISHED |
| Object |
Linares
Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
|
E328051
|
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: Linares | Statement: [Bailén, isLocatedNear, Linares]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linares Context triple: [Bailén, isLocatedNear, Linares]
-
A.
Linares
Linares is a provincial capital and agricultural city in Chile’s Maule Region, known for its surrounding farmlands and wine production.
-
B.
Linares
Linares is a Spanish football club known for being one of the early teams in the playing career of manager Rafael Benítez.
-
C.
Lucena
Lucena is a coastal city in the Philippines that serves as the capital and commercial hub of Quezon Province in the Southern Tagalog region.
-
D.
Linares y Pombo
Linares y Pombo is the compound Spanish surname of Arsenio Linares y Pombo, a notable Spanish military officer and politician of the late 19th and early 20th centuries.
-
E.
Durán
Durán is an Ecuadorian city in the Guayas Province, located across the Guayas River from Guayaquil and serving as an important transport and industrial hub.
- 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: Linares Triple: [Bailén, isLocatedNear, Linares]
Generated description
Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Linares Target entity description: Linares is a city in the province of Jaén in Andalusia, southern Spain, historically known for its mining industry and cultural heritage.
-
A.
Linares
Linares is a provincial capital and agricultural city in Chile’s Maule Region, known for its surrounding farmlands and wine production.
-
B.
Linares
Linares is a Spanish football club known for being one of the early teams in the playing career of manager Rafael Benítez.
-
C.
Lucena
Lucena is a historic city in the province of Córdoba, Andalusia, southern Spain, known for its rich cultural heritage and former Jewish community.
-
D.
Lucena
Lucena is a coastal city in the Philippines that serves as the capital and commercial hub of Quezon Province in the Southern Tagalog region.
-
E.
Linares y Pombo
Linares y Pombo is the compound Spanish surname of Arsenio Linares y Pombo, a notable Spanish military officer and politician of the late 19th and early 20th centuries.
- 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_69ad857dc98481909e585dc3372e3ed5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada269a9188190aada5b3799d4dfd7 |
completed | March 8, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2037cc5fc819084a441ebb045142b |
completed | March 12, 2026, 12:06 a.m. |
| NEDg | Description generation | batch_69b20412e6f8819097f30e50a4141cbe |
completed | March 12, 2026, 12:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b207e671888190ab8d97ad661bb5bd |
completed | March 12, 2026, 12:25 a.m. |
Created at: March 8, 2026, 3:03 p.m.