Triple

T24567296
Position Surface form Disambiguated ID Type / Status
Subject Gibson County, Tennessee E607825 entity
Predicate bordersCounty P6346 FINISHED
Object Weakley County
Weakley County is a rural county in northwestern Tennessee known for its agricultural economy and as the home of the University of Tennessee at Martin.
E1698511 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: Weakley County | Statement: [Gibson County, Tennessee, bordersCounty, Weakley 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: Weakley County
Triple: [Gibson County, Tennessee, bordersCounty, Weakley County]
Generated description
Weakley County is a rural county in northwestern Tennessee known for its agricultural economy and as the home of the University of Tennessee at Martin.

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_69e2c4cc35a48190990b7571bc086df8 completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a92141f88190bcbd7c6aeafc0725 completed April 30, 2026, 12:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10d9d5939c819082c990c7e8a613fb completed May 22, 2026, 10:33 p.m.
NEDg Description generation batch_6a10daf457748190b591c0db813105f2 completed May 22, 2026, 10:38 p.m.
NED2 Entity disambiguation (via description) batch_6a10dc7a3a50819089ed854ac6463fe6 completed May 22, 2026, 10:45 p.m.
Created at: April 18, 2026, 2:28 a.m.