Triple

T30511277
Position Surface form Disambiguated ID Type / Status
Subject One for the Money E776430 entity
Predicate portrays P264 FINISHED
Object Debbie Reynolds as Grandma Mazur
Debbie Reynolds as Grandma Mazur is the lively, sharp-tongued grandmother character she plays in the film adaptation of Janet Evanovich’s crime-comedy novel series.
 E1917514 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: Debbie Reynolds as Grandma Mazur | Statement: [One for the Money, portrays, Debbie Reynolds as Grandma Mazur]
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: Debbie Reynolds as Grandma Mazur
Triple: [One for the Money, portrays, Debbie Reynolds as Grandma Mazur]
Generated description
Debbie Reynolds as Grandma Mazur is the lively, sharp-tongued grandmother character she plays in the film adaptation of Janet Evanovich’s crime-comedy novel series.

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_69f2249a155c8190b1d512106007e9bb completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f687b9ebc881909b90ddefcf0668ed completed May 2, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27ac3b26708190a77764a23dff69b5 completed June 9, 2026, 6:01 a.m.
NEDg Description generation batch_6a27ae790df481908f7f4daf908c71cd completed June 9, 2026, 6:11 a.m.
NED2 Entity disambiguation (via description) batch_6a27afb73dec81908a2aab12940f4ab2 completed June 9, 2026, 6:16 a.m.
Created at: April 29, 2026, 8:16 p.m.