Triple

T30026465

Position	Surface form	Disambiguated ID	Type / Status
Subject	North Baltimore	`E762893`	entity
Predicate	hasPublicTransit	`P522`	FINISHED
Object	Light RailLink Mount Washington station Light RailLink Mount Washington station is a light rail transit stop serving the Mount Washington area in North Baltimore, Maryland.	`E1897499`	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: Light RailLink Mount Washington station | Statement: [North Baltimore, hasPublicTransit, Light RailLink Mount Washington 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: Light RailLink Mount Washington station
Triple: [North Baltimore, hasPublicTransit, Light RailLink Mount Washington station]

Generated description

Light RailLink Mount Washington station is a light rail transit stop serving the Mount Washington area in North Baltimore, Maryland.

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_69f2246ee6e48190b69e837b913b398a`	completed	April 29, 2026, 3:31 p.m.
NER	Named-entity recognition	`batch_69f679ac62ac8190b1ab93bf5803e0a9`	completed	May 2, 2026, 10:24 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a273231a4d48190ada158d1f54bbaf1`	completed	June 8, 2026, 9:20 p.m.
NEDg	Description generation	`batch_6a27369f226081909c756c1db665159a`	completed	June 8, 2026, 9:39 p.m.
NED2	Entity disambiguation (via description)	`batch_6a2736f83b08819084c8fadd1fe1b6f2`	completed	June 8, 2026, 9:41 p.m.

Created at: April 29, 2026, 6:48 p.m.