Triple

T33781125
Position Surface form Disambiguated ID Type / Status
Subject The Possession of Hannah Grace E865658 entity
Predicate mainCharacter P1183 FINISHED
Object Megan Reed
Megan Reed is the troubled ex-cop turned night-shift morgue attendant who confronts a malevolent force in the horror film "The Possession of Hannah Grace."
E2092234 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: Megan Reed | Statement: [The Possession of Hannah Grace, mainCharacter, Megan Reed]
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: Megan Reed
Triple: [The Possession of Hannah Grace, mainCharacter, Megan Reed]
Generated description
Megan Reed is the troubled ex-cop turned night-shift morgue attendant who confronts a malevolent force in the horror film "The Possession of Hannah Grace."

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_69f3498ecc2c8190bcd85e3f11dc215e completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6fcc8b3bc8190939a334c23e2ffa2 completed May 3, 2026, 7:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36f9aa31d4819083078cbef789b200 completed June 20, 2026, 8:35 p.m.
NEDg Description generation batch_6a36fa7cf07c819080d18e1c419568f7 completed June 20, 2026, 8:39 p.m.
NED2 Entity disambiguation (via description) batch_6a36fe233bb8819096419ecbfeebc79b completed June 20, 2026, 8:54 p.m.
Created at: May 1, 2026, 1:45 a.m.