Triple

T29522997
Position Surface form Disambiguated ID Type / Status
Subject Marlowe (2022 film) E748985 entity
Predicate basedOn P98 FINISHED
Object The Black-Eyed Blonde
The Black-Eyed Blonde is a 2014 hardboiled detective novel by John Banville (writing as Benjamin Black) that continues Raymond Chandler’s Philip Marlowe series.
 E1871556 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 Black-Eyed Blonde | Statement: [Marlowe (2022 film), basedOn, The Black-Eyed Blonde]
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 Black-Eyed Blonde
Triple: [Marlowe (2022 film), basedOn, The Black-Eyed Blonde]
Generated description
The Black-Eyed Blonde is a 2014 hardboiled detective novel by John Banville (writing as Benjamin Black) that continues Raymond Chandler’s Philip Marlowe 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_69f0bd46d99c81908ba9d01cc1dbef7d completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66c9a67008190902db0dc7185f54f completed May 2, 2026, 9:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260c2d2564819090392a3e2fb037e2 completed June 8, 2026, 12:26 a.m.
NEDg Description generation batch_6a26108e78fc8190b35e3ec5df7b0c8a completed June 8, 2026, 12:45 a.m.
NED2 Entity disambiguation (via description) batch_6a2614d0e1c08190b057693cf0a339de completed June 8, 2026, 1:03 a.m.
Created at: April 28, 2026, 4:43 p.m.