Triple

T24159081
Position Surface form Disambiguated ID Type / Status
Subject Bethel Airport E598778 entity
Predicate serves P98 FINISHED
Object western Alaska
Western Alaska is a remote, sparsely populated region of the U.S. state of Alaska characterized by tundra landscapes, subarctic climate, and communities largely accessible by air and water rather than road.
 E30198 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: western Alaska | Statement: [Bethel Airport, serves, western Alaska]
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: western Alaska
Triple: [Bethel Airport, serves, western Alaska]
Generated description
Western Alaska is a remote, sparsely populated region of the U.S. state of Alaska characterized by tundra landscapes, subarctic climate, and communities largely accessible by air and water rather than road.

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_69e288cb0a3081909ef221744f274384 completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1e0e7f8e481909c55ae66bf7b16e9 completed April 29, 2026, 10:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9a6c42c8190912b3f5fa2cb446f completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fccb3cfa48190919bcc0dcfbf232e completed May 22, 2026, 3:25 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcd0f4058819098819e7505bcf272 completed May 22, 2026, 3:27 a.m.
Created at: April 17, 2026, 11:31 p.m.