Triple

T22405768
Position Surface form Disambiguated ID Type / Status
Subject siege of A Coruña E553876 entity
Predicate opposingCommander P1698 FINISHED
Object Juan Pacheco y Osorio
Juan Pacheco y Osorio was a Spanish military commander known for leading royal forces in key engagements such as the defense during the siege of A Coruña.
E1712764 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: Juan Pacheco y Osorio | Statement: [siege of A Coruña, opposingCommander, Juan Pacheco y Osorio]
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: Juan Pacheco y Osorio
Triple: [siege of A Coruña, opposingCommander, Juan Pacheco y Osorio]
Generated description
Juan Pacheco y Osorio was a Spanish military commander known for leading royal forces in key engagements such as the defense during the siege of A Coruña.

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_69e11e4da7048190b4387d422a9a0de5 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f158b739b88190a7f1f20fd1e9b188 completed April 29, 2026, 1:02 a.m.
NED1 Entity disambiguation (via context triple) batch_6a118532500c819090062bcd7f3eeb8f completed May 23, 2026, 10:45 a.m.
NEDg Description generation batch_6a1185c3841081909a717baf5f3a38fb completed May 23, 2026, 10:47 a.m.
NED2 Entity disambiguation (via description) batch_6a11864330048190a6b55f72fb7c89c1 completed May 23, 2026, 10:49 a.m.
Created at: April 16, 2026, 8:46 p.m.