Triple

T28933626

Position	Surface form	Disambiguated ID	Type / Status
Subject	Bedford Park, Bronx	`E733853`	entity
Predicate	hasCommuterRailStationNearby	`P149101`	FINISHED
Object	Botanical Garden station Botanical Garden station is a Metro-North Railroad commuter rail stop in the Bronx serving visitors to the New York Botanical Garden and the surrounding Bedford Park neighborhood.	`E1843109`	NE FINISHED

How this triple was built (3 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: Botanical Garden station | Statement: [Bedford Park, Bronx, hasCommuterRailStationNearby, Botanical Garden station]

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: Botanical Garden station
Triple: [Bedford Park, Bronx, hasCommuterRailStationNearby, Botanical Garden station]

Generated description

Botanical Garden station is a Metro-North Railroad commuter rail stop in the Bronx serving visitors to the New York Botanical Garden and the surrounding Bedford Park neighborhood.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: hasCommuterRailStationNearby
Context triple: [Bedford Park, Bronx, hasCommuterRailStationNearby, Botanical Garden station]

A. nearCommuterRailStation chosen
Indicates that one entity is located close to, or within a short walking distance of, a commuter rail station.
B. hasNearbyRailwayStation
Indicates that a railway station is located within a short or convenient distance from the referenced entity.
C. hasMBTACommuterRailTerminus
Indicates that one entity serves as the endpoint or final stop of an MBTA Commuter Rail line for the other entity.
D. hasPublicTransportStop
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
E. hasNearbyRailway
Indicates that one entity is located close to a railway associated with or relevant to another entity.
F. None of above.

Provenance (6 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_69f05b0b49b08190b8994b339c7980f6`	completed	April 28, 2026, 7 a.m.
NER	Named-entity recognition	`batch_6a000fda03948190881b7275f249768f`	completed	May 10, 2026, 4:55 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a24ec3df8e481908ce469268723a3e8`	completed	June 7, 2026, 3:57 a.m.
NEDg	Description generation	`batch_6a24f741d0d08190932654cd45c92ec0`	completed	June 7, 2026, 4:44 a.m.
NED2	Entity disambiguation (via description)	`batch_6a24fafc00d481908d618fdc709806d6`	completed	June 7, 2026, 5 a.m.
PD	Predicate disambiguation	`batch_6a000f607f1881908ee750d58da91690`	completed	May 10, 2026, 4:53 a.m.

Created at: April 28, 2026, 8:30 a.m.