Triple

T25915908
Position Surface form Disambiguated ID Type / Status
Subject Greens Farms station E653035 entity
Predicate serves P98 FINISHED
Object Greens Farms neighborhood
Greens Farms neighborhood is a coastal residential area in Westport, Connecticut, known for its historic character, affluent homes, and commuter access to New York City.
E1701594 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: Greens Farms neighborhood | Statement: [Greens Farms station, serves, Greens Farms neighborhood]
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: Greens Farms neighborhood
Triple: [Greens Farms station, serves, Greens Farms neighborhood]
Generated description
Greens Farms neighborhood is a coastal residential area in Westport, Connecticut, known for its historic character, affluent homes, and commuter access to New York City.

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_69e7ab3e025c819086771607157f0015 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f603e445a881909fa29c2bb1f9b689 completed May 2, 2026, 2:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10eccf196c8190b2fe9b1a2d004c85 completed May 22, 2026, 11:54 p.m.
NEDg Description generation batch_6a10f0db7dc081909eb0d0158fccac1a completed May 23, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a10f1334640819093a1090416c94ee7 completed May 23, 2026, 12:13 a.m.
Created at: April 22, 2026, 8:31 a.m.