Triple

T32851092
Position Surface form Disambiguated ID Type / Status
Subject James Le Gros E840252 entity
Predicate notableRole P22 FINISHED
Object Wade Messer in Justified
Wade Messer in Justified is a recurring small-time criminal and sometimes informant in the TV series "Justified," known for his uneasy alliances and involvement in Harlan County’s criminal underworld.
E2026742 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: Wade Messer in Justified | Statement: [James Le Gros, notableRole, Wade Messer in Justified]
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: Wade Messer in Justified
Triple: [James Le Gros, notableRole, Wade Messer in Justified]
Generated description
Wade Messer in Justified is a recurring small-time criminal and sometimes informant in the TV series "Justified," known for his uneasy alliances and involvement in Harlan County’s criminal underworld.

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_69f349412c78819084459850e11d29f7 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6ce786af4819082f159abf7bef62c completed May 3, 2026, 4:26 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bd06f2e0819082d4606b5b05c727 completed June 19, 2026, 3:52 a.m.
NEDg Description generation batch_6a34c0ec31e081909520622a618d1ee5 completed June 19, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_6a34c14d0f448190981d7e18216e823f completed June 19, 2026, 4:10 a.m.
Created at: May 1, 2026, 1:17 a.m.