Triple

T38054907
Position Surface form Disambiguated ID Type / Status
Subject Ronkonkoma station E949872 entity
Predicate hasSignageStyle P14486 FINISHED
Object MTA LIRR standard signage
MTA LIRR standard signage is the uniform system of station and wayfinding signs used across the Long Island Rail Road, featuring consistent typography, colors, and layout to guide passengers.
E2254593 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: MTA LIRR standard signage | Statement: [Ronkonkoma station, hasSignageStyle, MTA LIRR standard signage]
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: MTA LIRR standard signage
Triple: [Ronkonkoma station, hasSignageStyle, MTA LIRR standard signage]
Generated description
MTA LIRR standard signage is the uniform system of station and wayfinding signs used across the Long Island Rail Road, featuring consistent typography, colors, and layout to guide passengers.

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_69f76f000cf081908c11fb5443b392e6 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbca02071c81908a50b7472549980e completed May 6, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a415d3315e8819088a67cce7ec138e7 completed June 28, 2026, 5:43 p.m.
NEDg Description generation batch_6a415dfc9b308190b75033cd89dd1a1f completed June 28, 2026, 5:46 p.m.
NED2 Entity disambiguation (via description) batch_6a415f4dfcf4819080739f521d4af061 completed June 28, 2026, 5:52 p.m.
Created at: May 3, 2026, 4:21 p.m.