Triple

T26475823
Position Surface form Disambiguated ID Type / Status
Subject Dorothy Salisbury Davis E666029 entity
Predicate notableWork P4 FINISHED
Object Where the Dark Streets Go
"Where the Dark Streets Go" is a crime novel by American mystery writer Dorothy Salisbury Davis, known for its psychologically rich characters and atmospheric suspense.
 E1725841 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: Where the Dark Streets Go | Statement: [Dorothy Salisbury Davis, notableWork, Where the Dark Streets Go]
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: Where the Dark Streets Go
Triple: [Dorothy Salisbury Davis, notableWork, Where the Dark Streets Go]
Generated description
"Where the Dark Streets Go" is a crime novel by American mystery writer Dorothy Salisbury Davis, known for its psychologically rich characters and atmospheric suspense.

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_69ee883f80dc819090e311b022b78e02 completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f612cce1348190861a76259a2b9c85 completed May 2, 2026, 3:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11aee7f320819083e3359d1238ab65 completed May 23, 2026, 1:43 p.m.
NEDg Description generation batch_6a11b048fe5081909c11c8996d4418af completed May 23, 2026, 1:48 p.m.
NED2 Entity disambiguation (via description) batch_6a11b18b6fdc8190801e1a7b3a296672 completed May 23, 2026, 1:54 p.m.
Created at: April 27, 2026, 12:23 a.m.