Triple

T25630607
Position Surface form Disambiguated ID Type / Status
Subject Barranco Hondo E642563 entity
Predicate partOf P40 FINISHED
Object Municipality of El Rosario
The Municipality of El Rosario is a local administrative region on the island of Tenerife in Spain’s Canary Islands, encompassing several towns and rural areas including Barranco Hondo.
E1692500 NE FINISHED

How this triple was built (2 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: Municipality of El Rosario | Statement: [Barranco Hondo, partOf, Municipality of El Rosario]
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: Municipality of El Rosario
Triple: [Barranco Hondo, partOf, Municipality of El Rosario]
Generated description
The Municipality of El Rosario is a local administrative region on the island of Tenerife in Spain’s Canary Islands, encompassing several towns and rural areas including Barranco Hondo.

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_69e77e7bd4548190a0c691b8a2f27ff1 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fa5d11c08190a33d2e81206343a8 completed May 2, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10cbe43f5481908cff6512bd250088 completed May 22, 2026, 9:34 p.m.
NEDg Description generation batch_6a10cc81bb8881909413a1b8924a0fe2 completed May 22, 2026, 9:37 p.m.
NED2 Entity disambiguation (via description) batch_6a10cd0fbcc08190a12ded88d999feab completed May 22, 2026, 9:39 p.m.
Created at: April 21, 2026, 5:17 p.m.