Triple

T33883118
Position Surface form Disambiguated ID Type / Status
Subject Unadilla Township, Michigan E868548 entity
Predicate hasCommunity P2605 FINISHED
Object Gregory, Michigan
Gregory, Michigan is a small unincorporated community and census-designated place in Livingston County known for its rural character and proximity to several lakes and recreation areas.
E2286140 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: Gregory, Michigan | Statement: [Unadilla Township, Michigan, hasCommunity, Gregory, Michigan]
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: Gregory, Michigan
Triple: [Unadilla Township, Michigan, hasCommunity, Gregory, Michigan]
Generated description
Gregory, Michigan is a small unincorporated community and census-designated place in Livingston County known for its rural character and proximity to several lakes and recreation areas.

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_69f34995b81c8190acdb45cea5a10eff completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7010c66f0819091bcb107aa41a1e4 completed May 3, 2026, 8:02 a.m.
NED1 Entity disambiguation (via context triple) batch_6a464f1c36288190a0d6373f6b3bff4a completed July 2, 2026, 11:44 a.m.
NEDg Description generation batch_6a464feae5a08190ab244199838161ec completed July 2, 2026, 11:47 a.m.
NED2 Entity disambiguation (via description) batch_6a4650f219788190945e7fdafd043cc9 completed July 2, 2026, 11:52 a.m.
Created at: May 1, 2026, 1:48 a.m.