Triple

T34104144
Position Surface form Disambiguated ID Type / Status
Subject Cartel E874652 entity
Predicate notableMember P10 FINISHED
Object Marco Salamanca
Marco Salamanca is a fictional hitman and one of the silent, twin enforcers of the Juárez Cartel in the television series Breaking Bad and its prequel Better Call Saul.
E1606604 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: Marco Salamanca | Statement: [Cartel, notableMember, Marco Salamanca]
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: Marco Salamanca
Triple: [Cartel, notableMember, Marco Salamanca]
Generated description
Marco Salamanca is a fictional hitman and one of the silent, twin enforcers of the Juárez Cartel in the television series Breaking Bad and its prequel Better Call Saul.

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_69f349a80d4481908527317d43f5c579 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f70ca7d9348190960c9ee97ddd54ea completed May 3, 2026, 8:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3704807fec8190912ff68cbaff5bed completed June 20, 2026, 9:22 p.m.
NEDg Description generation batch_6a370543b0c08190a81fe42444b9fbe6 completed June 20, 2026, 9:25 p.m.
NED2 Entity disambiguation (via description) batch_6a3705f992b4819080ee9743fab5932d completed June 20, 2026, 9:28 p.m.
Created at: May 1, 2026, 1:53 a.m.