Triple

T28985965
Position Surface form Disambiguated ID Type / Status
Subject Gerardo Fernández Albor E734681 entity
Predicate familyName P18 FINISHED
Object Fernández Albor
Fernández Albor is the surname of Gerardo Fernández Albor, a Spanish physician and politician who served as the first democratically elected president of Galicia’s regional government.
E1946147 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: Fernández Albor | Statement: [Gerardo Fernández Albor, familyName, Fernández Albor]
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: Fernández Albor
Triple: [Gerardo Fernández Albor, familyName, Fernández Albor]
Generated description
Fernández Albor is the surname of Gerardo Fernández Albor, a Spanish physician and politician who served as the first democratically elected president of Galicia’s regional government.

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_69f05b0dd9b481908b7901e1c95ff6b2 completed April 28, 2026, 7 a.m.
NER Named-entity recognition batch_69f65f7994088190bab22373bcce6847 completed May 2, 2026, 8:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a293882cd14819083e8fbcff0e5499b completed June 10, 2026, 10:12 a.m.
NEDg Description generation batch_6a29397f80108190b735df8c27bed113 completed June 10, 2026, 10:16 a.m.
NED2 Entity disambiguation (via description) batch_6a2939f6054c8190916e6b8cbdf98c55 completed June 10, 2026, 10:18 a.m.
Created at: April 28, 2026, 9:14 a.m.