Triple

T37152501
Position Surface form Disambiguated ID Type / Status
Subject Texas Chainsaw Massacre (2022 film) E920396 entity
Predicate screenwriter P2831 FINISHED
Object Chris Thomas Devlin
Chris Thomas Devlin is an American screenwriter best known for writing the 2022 reboot of the horror franchise "Texas Chainsaw Massacre."
E2216625 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: Chris Thomas Devlin | Statement: [Texas Chainsaw Massacre (2022 film), screenwriter, Chris Thomas Devlin]
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: Chris Thomas Devlin
Triple: [Texas Chainsaw Massacre (2022 film), screenwriter, Chris Thomas Devlin]
Generated description
Chris Thomas Devlin is an American screenwriter best known for writing the 2022 reboot of the horror franchise "Texas Chainsaw Massacre."

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_69f76e9f87c08190b4c8f7fafbd8345a completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb308da2208190a803ca39ce3bade9 completed May 6, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a402bb0bfd48190a931193da162e3f5 completed June 27, 2026, 7:59 p.m.
NEDg Description generation batch_6a402e7e79bc81909237840dbc7ac787 completed June 27, 2026, 8:11 p.m.
NED2 Entity disambiguation (via description) batch_6a402f1d63488190854b93815f522d3a completed June 27, 2026, 8:14 p.m.
Created at: May 3, 2026, 4:15 p.m.