Triple

T25268978
Position Surface form Disambiguated ID Type / Status
Subject Jellico, Tennessee E633511 entity
Predicate isServedBy P1293 FINISHED
Object Jellico Municipal Airport
Jellico Municipal Airport is a small public-use airport that provides general aviation services to the city of Jellico in northeastern Tennessee.
E1723470 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: Jellico Municipal Airport | Statement: [Jellico, Tennessee, isServedBy, Jellico Municipal Airport]
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: Jellico Municipal Airport
Triple: [Jellico, Tennessee, isServedBy, Jellico Municipal Airport]
Generated description
Jellico Municipal Airport is a small public-use airport that provides general aviation services to the city of Jellico in northeastern Tennessee.

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_69e75a92f48881909974ff9c11150a2e completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f48ba023b481908b6c6caf212818a3 completed May 1, 2026, 11:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a11ae897e64819091f6813221e77860 completed May 23, 2026, 1:41 p.m.
NEDg Description generation batch_6a11af2076908190b275c87caa60bb7c completed May 23, 2026, 1:44 p.m.
NED2 Entity disambiguation (via description) batch_6a11afbb49c48190a2640fa6e8186fd8 completed May 23, 2026, 1:46 p.m.
Created at: April 21, 2026, 1:16 p.m.