Triple

T32172491
Position Surface form Disambiguated ID Type / Status
Subject Paul Zaza E821746 entity
Predicate composedForFilm P29643 FINISHED
Object The Pink Chiquitas (1987)
The Pink Chiquitas (1987) is a low-budget Canadian sci-fi comedy film about a mysterious pink meteorite that turns women into seductive, man-hunting vixens in a small town.
 E1994619 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: The Pink Chiquitas (1987) | Statement: [Paul Zaza, composedForFilm, The Pink Chiquitas (1987)]
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: The Pink Chiquitas (1987)
Triple: [Paul Zaza, composedForFilm, The Pink Chiquitas (1987)]
Generated description
The Pink Chiquitas (1987) is a low-budget Canadian sci-fi comedy film about a mysterious pink meteorite that turns women into seductive, man-hunting vixens in a small town.

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_69f3490699a48190bbef96b198e8fade completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6ba75e89481908163f7c227094a4e completed May 3, 2026, 3:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f0bdaca388190ac7667ebccd1e649 completed June 14, 2026, 8:15 p.m.
NEDg Description generation batch_6a2f0c5dba788190b3ba409c76fcf63b completed June 14, 2026, 8:17 p.m.
NED2 Entity disambiguation (via description) batch_6a2f0cdc1b048190a34f78a36bf5bb0a completed June 14, 2026, 8:19 p.m.
Created at: May 1, 2026, 12:33 a.m.