Triple

T35847883
Position Surface form Disambiguated ID Type / Status
Subject Levelland, Texas E1036262 entity
Predicate hasNearbyAirport P4363 FINISHED
Object Levelland Municipal Airport
Levelland Municipal Airport is a public-use airport serving general aviation needs for the city of Levelland in Hockley County, Texas.
E2159001 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: Levelland Municipal Airport | Statement: [Levelland, Texas, hasNearbyAirport, Levelland 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: Levelland Municipal Airport
Triple: [Levelland, Texas, hasNearbyAirport, Levelland Municipal Airport]
Generated description
Levelland Municipal Airport is a public-use airport serving general aviation needs for the city of Levelland in Hockley County, Texas.

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_69f76e1a29e8819088280f26096aeb55 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a94e993081909cc5a1273f3c2e81 completed May 3, 2026, 8 p.m.
NED1 Entity disambiguation (via context triple) batch_6a389c2af8188190837e542578cf6d9a completed June 22, 2026, 2:21 a.m.
NEDg Description generation batch_6a389edcb9548190b66de42ee585319e completed June 22, 2026, 2:33 a.m.
NED2 Entity disambiguation (via description) batch_6a38a00b69648190b4ce418ac8d9e33a completed June 22, 2026, 2:38 a.m.
Created at: May 3, 2026, 4:06 p.m.