Triple

T30728600
Position Surface form Disambiguated ID Type / Status
Subject Danny Reagan E782350 entity
Predicate department P1467 FINISHED
Object NYPD Major Case Squad
The NYPD Major Case Squad is a specialized New York City Police Department unit that investigates high-profile and complex crimes such as major robberies, kidnappings, and homicides.
 E1929300 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: NYPD Major Case Squad | Statement: [Danny Reagan, department, NYPD Major Case Squad]
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: NYPD Major Case Squad
Triple: [Danny Reagan, department, NYPD Major Case Squad]
Generated description
The NYPD Major Case Squad is a specialized New York City Police Department unit that investigates high-profile and complex crimes such as major robberies, kidnappings, and homicides.

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_69f224ad9f9c81908e02a79ae0001137 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68ee186508190894808b23be1d88d completed May 2, 2026, 11:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28990fee4081909b3611ffbfbab7e0 completed June 9, 2026, 10:52 p.m.
NEDg Description generation batch_6a289bd47cb08190a37851914e6d8417 completed June 9, 2026, 11:03 p.m.
NED2 Entity disambiguation (via description) batch_6a289c6b2ad481909dd61a0735dff305 completed June 9, 2026, 11:06 p.m.
Created at: April 29, 2026, 8:37 p.m.