Triple

T36509657

Position	Surface form	Disambiguated ID	Type / Status
Subject	Mott Haven	`E899862`	entity
Predicate	fireService	`P3910`	FINISHED
Object	FDNY Battalion 14 FDNY Battalion 14 is a New York City Fire Department battalion responsible for coordinating firefighting and emergency response operations in the Mott Haven area of the Bronx.	`E2189301`	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: FDNY Battalion 14 | Statement: [Mott Haven, fireService, FDNY Battalion 14]

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: FDNY Battalion 14
Triple: [Mott Haven, fireService, FDNY Battalion 14]

Generated description

FDNY Battalion 14 is a New York City Fire Department battalion responsible for coordinating firefighting and emergency response operations in the Mott Haven area of the Bronx.

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_69f76e5dada881909da2d34bc7a9202a`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7c1edc644819091c04704fb99b5bb`	completed	May 3, 2026, 9:45 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a39e6d62f2c8190a2f2576cdadbd1bf`	completed	June 23, 2026, 1:52 a.m.
NEDg	Description generation	`batch_6a39eab7472c8190a1a7f6946bde9c73`	completed	June 23, 2026, 2:08 a.m.
NED2	Entity disambiguation (via description)	`batch_6a39ecc121288190911c2035957fb988`	completed	June 23, 2026, 2:17 a.m.

Created at: May 3, 2026, 4:10 p.m.