Triple

T30170308
Position Surface form Disambiguated ID Type / Status
Subject Pedro González de Mendoza E766901 entity
Predicate fullName P16 FINISHED
Object Pedro González de Mendoza
Pedro González de Mendoza was a powerful 15th-century Spanish cardinal and statesman who served the Catholic Monarchs and played a key role in the unification of Spain.
E1907330 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: Pedro González de Mendoza | Statement: [Pedro González de Mendoza, fullName, Pedro González de Mendoza]
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: Pedro González de Mendoza
Triple: [Pedro González de Mendoza, fullName, Pedro González de Mendoza]
Generated description
Pedro González de Mendoza was a powerful 15th-century Spanish cardinal and statesman who served the Catholic Monarchs and played a key role in the unification of Spain.

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_69f2247a968881909d79c18f2bfcb275 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f0b25908190baf7f9dfef6ec6ce completed May 2, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a276ee3af648190ae8a0e39052fd996 completed June 9, 2026, 1:39 a.m.
NEDg Description generation batch_6a276febe8e48190a61b0e20ac44ab06 completed June 9, 2026, 1:44 a.m.
NED2 Entity disambiguation (via description) batch_6a27708bfc588190abd7fa5039f5153a completed June 9, 2026, 1:46 a.m.
Created at: April 29, 2026, 7:24 p.m.