Triple

T27064254
Position Surface form Disambiguated ID Type / Status
Subject The Grudge 2 E685129 entity
Predicate mainCharacter P1183 FINISHED
Object Vanessa
Vanessa is a central character in the horror film "The Grudge 2," whose experiences help drive the movie’s supernatural storyline.
E1758984 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: Vanessa | Statement: [The Grudge 2, mainCharacter, Vanessa]
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: Vanessa
Triple: [The Grudge 2, mainCharacter, Vanessa]
Generated description
Vanessa is a central character in the horror film "The Grudge 2," whose experiences help drive the movie’s supernatural storyline.

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_69ef14835fcc81908bd737b4267ae528 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f622e734a881908ac15e64575c69d4 completed May 2, 2026, 4:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1247f9eb5881908278ff3bc201f5ea completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a124bf692708190b52de3df629cb45c completed May 24, 2026, 12:53 a.m.
NED2 Entity disambiguation (via description) batch_6a124c6c11f081908fc85103770dcf9c completed May 24, 2026, 12:55 a.m.
Created at: April 27, 2026, 8:23 a.m.