Triple

T36879801
Position Surface form Disambiguated ID Type / Status
Subject The Gun Ranger E911445 entity
Predicate screenwriter P2831 FINISHED
Object Nate Gatzert
Nate Gatzert was an American screenwriter active during the early 20th century, known for his work on Western films.
E2202677 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: Nate Gatzert | Statement: [The Gun Ranger, screenwriter, Nate Gatzert]
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: Nate Gatzert
Triple: [The Gun Ranger, screenwriter, Nate Gatzert]
Generated description
Nate Gatzert was an American screenwriter active during the early 20th century, known for his work on Western films.

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_69f76e82339881909607a65c0503d941 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9fd68f6988190abe4a5471d0876e8 completed May 5, 2026, 2:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3dfaedb3388190bb3cdfd4bab08277 completed June 26, 2026, 4:07 a.m.
NEDg Description generation batch_6a3e020a7cd4819091f3e7c702115131 completed June 26, 2026, 4:37 a.m.
NED2 Entity disambiguation (via description) batch_6a3e0472733081908c285234143df57a completed June 26, 2026, 4:47 a.m.
Created at: May 3, 2026, 4:13 p.m.