Triple

T36241355
Position Surface form Disambiguated ID Type / Status
Subject Villa Reynolds Airport E891525 entity
Predicate alternativeName P39 FINISHED
Object Aeropuerto de Villa Reynolds
Aeropuerto de Villa Reynolds is a regional airport in Argentina that serves the town of Villa Reynolds and its surrounding area.
E2174698 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: Aeropuerto de Villa Reynolds | Statement: [Villa Reynolds Airport, alternativeName, Aeropuerto de Villa Reynolds]
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: Aeropuerto de Villa Reynolds
Triple: [Villa Reynolds Airport, alternativeName, Aeropuerto de Villa Reynolds]
Generated description
Aeropuerto de Villa Reynolds is a regional airport in Argentina that serves the town of Villa Reynolds and its surrounding area.

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_69f76e44993481908fa75e4c48d0aab3 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b5cf70dc8190a967c46a0bfe6965 completed May 3, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a394d41fdcc819091c2d7282987fb23 completed June 22, 2026, 2:57 p.m.
NEDg Description generation batch_6a39502c07748190b383c1f43106bc40 completed June 22, 2026, 3:09 p.m.
NED2 Entity disambiguation (via description) batch_6a3950bea3bc819086f7ed2405dae100 completed June 22, 2026, 3:11 p.m.
Created at: May 3, 2026, 4:09 p.m.