Triple

T34642029
Position Surface form Disambiguated ID Type / Status
Subject Michelle Pfeiffer as Claire Spencer E889585 entity
Predicate hasSpouseInStory P30304 FINISHED
Object Norman Spencer
Norman Spencer is a central character in the supernatural thriller film "What Lies Beneath," serving as Claire Spencer's husband whose dark secrets drive the plot.
E2108828 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: Norman Spencer | Statement: [Michelle Pfeiffer as Claire Spencer, hasSpouseInStory, Norman Spencer]
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: Norman Spencer
Triple: [Michelle Pfeiffer as Claire Spencer, hasSpouseInStory, Norman Spencer]
Generated description
Norman Spencer is a central character in the supernatural thriller film "What Lies Beneath," serving as Claire Spencer's husband whose dark secrets drive the plot.

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_69f349d724848190b63ad3407e0006d9 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f72292f4388190a72cd79d37a244e7 completed May 3, 2026, 10:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a375bd004a081908a03f177d65d5c22 completed June 21, 2026, 3:34 a.m.
NEDg Description generation batch_6a375c700ed08190be326445c1b8c905 completed June 21, 2026, 3:37 a.m.
NED2 Entity disambiguation (via description) batch_6a375cd6d01881909022ec9c7ff6895f completed June 21, 2026, 3:39 a.m.
Created at: May 1, 2026, 2:04 a.m.