Triple

T32579210
Position Surface form Disambiguated ID Type / Status
Subject Griggsville, Illinois E832732 entity
Predicate county P75 FINISHED
Object Pike County
Pike County is a largely rural county in western Illinois known for its agricultural landscape, small towns, and historic communities along the Mississippi and Illinois rivers.
E2141762 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: Pike County | Statement: [Griggsville, Illinois, county, Pike County]
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: Pike County
Triple: [Griggsville, Illinois, county, Pike County]
Generated description
Pike County is a largely rural county in western Illinois known for its agricultural landscape, small towns, and historic communities along the Mississippi and Illinois rivers.

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_69f349289adc81909f4374a58ec35a39 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c667f4a881908bf678f99f056a0c completed May 3, 2026, 3:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3840109cf8819089151d143da2e4e9 completed June 21, 2026, 7:48 p.m.
NEDg Description generation batch_6a3840d842a8819093075b6c8556b86f completed June 21, 2026, 7:51 p.m.
NED2 Entity disambiguation (via description) batch_6a38417151208190a130bdb18576e17e completed June 21, 2026, 7:54 p.m.
Created at: May 1, 2026, 1:04 a.m.