Triple

T25203343
Position Surface form Disambiguated ID Type / Status
Subject Springfield–Branson National Airport E631179 entity
Predicate hasRunway P105 FINISHED
Object Runway 14/32
Runway 14/32 is a primary paved runway at Springfield–Branson National Airport in Missouri, used for handling a range of commercial and general aviation aircraft operations.
 E1765584 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: Runway 14/32 | Statement: [Springfield–Branson National Airport, hasRunway, Runway 14/32]
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: Runway 14/32
Triple: [Springfield–Branson National Airport, hasRunway, Runway 14/32]
Generated description
Runway 14/32 is a primary paved runway at Springfield–Branson National Airport in Missouri, used for handling a range of commercial and general aviation aircraft operations.

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_69e75a8b86c4819089eda22c843b739f completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f474ba127c819086f8f0c698a1bb4d completed May 1, 2026, 9:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a129c750e048190bfc69cedb6ebbd7c completed May 24, 2026, 6:36 a.m.
NEDg Description generation batch_6a129d440e448190bad8b3e249c41698 completed May 24, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_6a129dac5d2081908e48a30357a8547f completed May 24, 2026, 6:41 a.m.
Created at: April 21, 2026, 12:51 p.m.