Triple

T31063290
Position Surface form Disambiguated ID Type / Status
Subject As Pontes de García Rodríguez E791602 entity
Predicate hasPowerStation P17850 FINISHED
Object As Pontes power station
As Pontes power station is a major thermal power plant in Galicia, Spain, historically one of the country’s largest electricity-generating facilities.
 E1945229 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: As Pontes power station | Statement: [As Pontes de García Rodríguez, hasPowerStation, As Pontes power station]
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: As Pontes power station
Triple: [As Pontes de García Rodríguez, hasPowerStation, As Pontes power station]
Generated description
As Pontes power station is a major thermal power plant in Galicia, Spain, historically one of the country’s largest electricity-generating facilities.

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_69f224cc0c5c81908404f087bff92997 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f695792b748190882e715603d406a2 completed May 3, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a292b1849508190814aff1942097066 completed June 10, 2026, 9:15 a.m.
NEDg Description generation batch_6a292bdbc818819084c95014803d3945 completed June 10, 2026, 9:18 a.m.
NED2 Entity disambiguation (via description) batch_6a292c909f1881908aa4f58707ae16da completed June 10, 2026, 9:21 a.m.
Created at: April 29, 2026, 9:01 p.m.