Triple

T28489050
Position Surface form Disambiguated ID Type / Status
Subject People's Alliance (Spain) E720910 entity
Predicate hasLeader P981 FINISHED
Object Antonio Hernández Mancha
Antonio Hernández Mancha is a Spanish politician and lawyer best known for briefly leading the conservative People's Alliance party in the late 1980s.
E1936908 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: Antonio Hernández Mancha | Statement: [People's Alliance (Spain), hasLeader, Antonio Hernández Mancha]
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: Antonio Hernández Mancha
Triple: [People's Alliance (Spain), hasLeader, Antonio Hernández Mancha]
Generated description
Antonio Hernández Mancha is a Spanish politician and lawyer best known for briefly leading the conservative People's Alliance party in the late 1980s.

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_69f01a5a47148190b0a7e111bc432e0a completed April 28, 2026, 2:24 a.m.
NER Named-entity recognition batch_69f64f135f3c8190969a0db54e262a03 completed May 2, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28c7a8258881909e6005d375901c65 completed June 10, 2026, 2:10 a.m.
NEDg Description generation batch_6a28ca5ca8dc81909225215a8a02bf6f completed June 10, 2026, 2:22 a.m.
NED2 Entity disambiguation (via description) batch_6a28cc6d10c48190b4d80bb129c95229 completed June 10, 2026, 2:31 a.m.
Created at: April 28, 2026, 3 a.m.