Triple

T29614261
Position Surface form Disambiguated ID Type / Status
Subject Machine Gun McCain E754816 entity
Predicate screenwriter P2831 FINISHED
Object Oreste Palella
Oreste Palella was an Italian screenwriter best known for his work on mid-20th-century crime and genre films, including the heist movie "Machine Gun McCain."
E2297302 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: Oreste Palella | Statement: [Machine Gun McCain, screenwriter, Oreste Palella]
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: Oreste Palella
Triple: [Machine Gun McCain, screenwriter, Oreste Palella]
Generated description
Oreste Palella was an Italian screenwriter best known for his work on mid-20th-century crime and genre films, including the heist movie "Machine Gun McCain."

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_69f0ef85f62081909842b59fdf8717e1 completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69f66e1fd06081909b920f2dae3bfd37 completed May 2, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a834f7435e8819088d14264b8567afb completed Aug. 17, 2026, 6:14 p.m.
NEDg Description generation batch_6a835900a23c8190b8d1c9f5de1a4497 completed Aug. 17, 2026, 6:54 p.m.
NED2 Entity disambiguation (via description) batch_6a83597fd29881908123e2c3c8c019d1 completed Aug. 17, 2026, 6:57 p.m.
Created at: April 28, 2026, 6:30 p.m.