Triple

T26892212
Position Surface form Disambiguated ID Type / Status
Subject Caleb Carr E677206 entity
Predicate notableWork P4 FINISHED
Object The Angel of Darkness
The Angel of Darkness is a historical crime novel by Caleb Carr that continues the adventures of the investigative team from The Alienist as they pursue a child kidnapper in 1890s New York City.
E1746641 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 Angel of Darkness | Statement: [Caleb Carr, notableWork, The Angel of Darkness]
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 Angel of Darkness
Triple: [Caleb Carr, notableWork, The Angel of Darkness]
Generated description
The Angel of Darkness is a historical crime novel by Caleb Carr that continues the adventures of the investigative team from The Alienist as they pursue a child kidnapper in 1890s New York City.

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_69eee9bc0c90819085608c8bdc513a57 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61f69e4508190ab20c3f2052282e7 completed May 2, 2026, 3:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a121ea2fa008190913b10dedb3b67a6 completed May 23, 2026, 9:39 p.m.
NEDg Description generation batch_6a121f90eec08190bd18be556349e464 completed May 23, 2026, 9:43 p.m.
NED2 Entity disambiguation (via description) batch_6a12205e89f4819098a8901d520e9c7d completed May 23, 2026, 9:47 p.m.
Created at: April 27, 2026, 5:45 a.m.