Triple

T36998501
Position Surface form Disambiguated ID Type / Status
Subject Hornafjörður E915290 entity
Predicate hasTransportInfrastructure P2560 FINISHED
Object Hornafjörður Airport
Hornafjörður Airport is a small regional airport in southeastern Iceland that serves the town of Höfn and the surrounding Hornafjörður area.
E2213200 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: Hornafjörður Airport | Statement: [Hornafjörður, hasTransportInfrastructure, Hornafjörður 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: Hornafjörður Airport
Triple: [Hornafjörður, hasTransportInfrastructure, Hornafjörður Airport]
Generated description
Hornafjörður Airport is a small regional airport in southeastern Iceland that serves the town of Höfn and the surrounding Hornafjörður 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_69f76e8f1a8c81909db172ed31304971 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9ffe947bc819087d24c891bfc5eb9 completed May 5, 2026, 2:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3efdae405481909c4896c31b4adb27 completed June 26, 2026, 10:31 p.m.
NEDg Description generation batch_6a3f48d894e88190a506bc35cb9869ee completed June 27, 2026, 3:51 a.m.
NED2 Entity disambiguation (via description) batch_6a3f493ec4e081909500a9a253c32d70 completed June 27, 2026, 3:53 a.m.
Created at: May 3, 2026, 4:14 p.m.