Triple

T32366942
Position Surface form Disambiguated ID Type / Status
Subject Sarah (It Comes at Night) E827019 entity
Predicate child P120 FINISHED
Object Travis (It Comes at Night)
Travis is the teenage son and central protagonist in the psychological horror film "It Comes at Night," whose fears and perceptions drive much of the movie’s tense, ambiguous atmosphere.
E2005821 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: Travis (It Comes at Night) | Statement: [Sarah (It Comes at Night), child, Travis (It Comes at Night)]
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: Travis (It Comes at Night)
Triple: [Sarah (It Comes at Night), child, Travis (It Comes at Night)]
Generated description
Travis is the teenage son and central protagonist in the psychological horror film "It Comes at Night," whose fears and perceptions drive much of the movie’s tense, ambiguous atmosphere.

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_69f349166d548190887b412fe908e2f4 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6be9e9e1881909c41e3189585cfd0 completed May 3, 2026, 3:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a344f0846748190aafd065be830a3c4 completed June 18, 2026, 8:03 p.m.
NEDg Description generation batch_6a34509a11e4819090df78444c483345 completed June 18, 2026, 8:10 p.m.
NED2 Entity disambiguation (via description) batch_6a3451b9c38481909f6e757bc72be84b completed June 18, 2026, 8:14 p.m.
Created at: May 1, 2026, 12:50 a.m.