Triple

T36755555
Position Surface form Disambiguated ID Type / Status
Subject Matando Cabos E908042 entity
Predicate director P255 FINISHED
Object Alejandro Lozano
Alejandro Lozano is a Mexican film director best known for his work on the dark comedy crime film "Matando Cabos."
E2287194 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: Alejandro Lozano | Statement: [Matando Cabos, director, Alejandro Lozano]
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: Alejandro Lozano
Triple: [Matando Cabos, director, Alejandro Lozano]
Generated description
Alejandro Lozano is a Mexican film director best known for his work on the dark comedy crime film "Matando Cabos."

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_69f76e779bec8190be0e1f87a131e0f4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7c94642448190ae9a2710822a2a96 completed May 3, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4767e2697481908712d82281a4a404 completed July 3, 2026, 7:42 a.m.
NEDg Description generation batch_6a476a4eb364819099e257a3b6bdb518 completed July 3, 2026, 7:52 a.m.
NED2 Entity disambiguation (via description) batch_6a476aaf619c81908c438b025fc4e3a1 completed July 3, 2026, 7:54 a.m.
Created at: May 3, 2026, 4:12 p.m.