Triple

T36249560

Position	Surface form	Disambiguated ID	Type / Status
Subject	Aniak Airport	`E891758`	entity
Predicate	region	`P40`	FINISHED
Object	western Alaska Western Alaska is a remote, sparsely populated region of the state characterized by tundra landscapes, subarctic climate, and communities that often rely on small regional airports and river transport.	`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: [Aniak Airport, region, 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: [Aniak Airport, region, western Alaska]

Generated description

Western Alaska is a remote, sparsely populated region of the state characterized by tundra landscapes, subarctic climate, and communities that often rely on small regional airports and river transport.

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_69f76e4599108190811532e707d6bc2c`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69f7b5f89c5c8190825ed5d4317c540c`	completed	May 3, 2026, 8:54 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a39b41ded7c81908a8ed6fcb7673e10`	completed	June 22, 2026, 10:15 p.m.
NEDg	Description generation	`batch_6a39b6f5241481909e49d882c5e51b22`	completed	June 22, 2026, 10:28 p.m.
NED2	Entity disambiguation (via description)	`batch_6a39b87500508190824552f54c2a5724`	completed	June 22, 2026, 10:34 p.m.

Created at: May 3, 2026, 4:09 p.m.