Triple

T32286967
Position Surface form Disambiguated ID Type / Status
Subject David Goodis E824860 entity
Predicate notableWork P4 FINISHED
Object The Moon in the Gutter
The Moon in the Gutter is a dark, melancholic crime novel by David Goodis that follows a dockworker obsessed with avenging his sister’s death in a grim urban waterfront setting.
 E2000964 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 Moon in the Gutter | Statement: [David Goodis, notableWork, The Moon in the Gutter]
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 Moon in the Gutter
Triple: [David Goodis, notableWork, The Moon in the Gutter]
Generated description
The Moon in the Gutter is a dark, melancholic crime novel by David Goodis that follows a dockworker obsessed with avenging his sister’s death in a grim urban waterfront setting.

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_69f349101b788190b4f14884dc7d1ed2 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bd2f061081909798c04674844492 completed May 3, 2026, 3:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3057039b3c819092f9ff7fcac1ab15 completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a3057bd1690819088277d98c9b78212 completed June 15, 2026, 7:51 p.m.
NED2 Entity disambiguation (via description) batch_6a305827285481909d8953d9e9e08831 completed June 15, 2026, 7:53 p.m.
Created at: May 1, 2026, 12:44 a.m.