Triple

T25922244
Position Surface form Disambiguated ID Type / Status
Subject Andrés de Olmos E653201 entity
Predicate notableWork P4 FINISHED
Object Vocabulario en lengua mexicana
Vocabulario en lengua mexicana is an early colonial-era Nahuatl (Mexican language) vocabulary compiled by the Franciscan friar and linguist Andrés de Olmos.
E1706382 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: Vocabulario en lengua mexicana | Statement: [Andrés de Olmos, notableWork, Vocabulario en lengua mexicana]
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: Vocabulario en lengua mexicana
Triple: [Andrés de Olmos, notableWork, Vocabulario en lengua mexicana]
Generated description
Vocabulario en lengua mexicana is an early colonial-era Nahuatl (Mexican language) vocabulary compiled by the Franciscan friar and linguist Andrés de Olmos.

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_69e7ab3e025c819086771607157f0015 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f603ea6ea081909c6223c5544f6992 completed May 2, 2026, 2:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111afe27848190af247773244cb795 completed May 23, 2026, 3:11 a.m.
NEDg Description generation batch_6a111bba8e5c819087fe7628a159309a completed May 23, 2026, 3:15 a.m.
NED2 Entity disambiguation (via description) batch_6a111c40813c8190b914862b78512c0f completed May 23, 2026, 3:17 a.m.
Created at: April 22, 2026, 8:32 a.m.