Triple

T27418342
Position Surface form Disambiguated ID Type / Status
Subject Vera Matson E692966 entity
Predicate coWriterOf P2389 FINISHED
Object Playing for Keeps
Playing for Keeps is a 1956 musical comedy film starring Elvis Presley as a young man who returns to his hometown to run a small hotel, leading to romantic entanglements and local conflicts.
 E1771629 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: Playing for Keeps | Statement: [Vera Matson, coWriterOf, Playing for Keeps]
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: Playing for Keeps
Triple: [Vera Matson, coWriterOf, Playing for Keeps]
Generated description
Playing for Keeps is a 1956 musical comedy film starring Elvis Presley as a young man who returns to his hometown to run a small hotel, leading to romantic entanglements and local conflicts.

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_69ef5208617081908f731d312e0fd1bc completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d1bacb881909671913b6d24b351 completed May 2, 2026, 4:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12b246776c81908a7d08f319780662 completed May 24, 2026, 8:09 a.m.
NEDg Description generation batch_6a12b2c102b881908d0c1299f6e1035d completed May 24, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_6a12b33dc9f881908cca1fd1b03c6c67 completed May 24, 2026, 8:13 a.m.
Created at: April 27, 2026, 12:35 p.m.