Triple

T29604538
Position Surface form Disambiguated ID Type / Status
Subject GOJ E754540 entity
Predicate alternativeName P39 FINISHED
Object Nizhny Novgorod International Airport
Nizhny Novgorod International Airport is a major commercial airport serving the city of Nizhny Novgorod and the surrounding Volga region in western Russia.
E1883006 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: Nizhny Novgorod International Airport | Statement: [GOJ, alternativeName, Nizhny Novgorod International 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: Nizhny Novgorod International Airport
Triple: [GOJ, alternativeName, Nizhny Novgorod International Airport]
Generated description
Nizhny Novgorod International Airport is a major commercial airport serving the city of Nizhny Novgorod and the surrounding Volga region in western Russia.

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_69f0ef84e5d08190a0df17f5930ceed3 completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69f66de5e2b88190bd2529cc77629609 completed May 2, 2026, 9:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26c8d4dc088190ba8b3b0c870e281f completed June 8, 2026, 1:51 p.m.
NEDg Description generation batch_6a26cccde768819084aeb6b6af7db79a completed June 8, 2026, 2:08 p.m.
NED2 Entity disambiguation (via description) batch_6a26d2ec512c8190aa76543315c0ddd2 completed June 8, 2026, 2:34 p.m.
Created at: April 28, 2026, 6:24 p.m.