Triple

T33247989
Position Surface form Disambiguated ID Type / Status
Subject airside secure area of Washington Dulles International Airport E851155 entity
Predicate hasPart P35 FINISHED
Object Concourse D
Concourse D is a passenger terminal concourse at Washington Dulles International Airport that serves multiple airline gates and amenities within the airport’s secure area.
E8349 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: Concourse D | Statement: [airside secure area of Washington Dulles International Airport, hasPart, Concourse D]
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: Concourse D
Triple: [airside secure area of Washington Dulles International Airport, hasPart, Concourse D]
Generated description
Concourse D is a passenger terminal concourse at Washington Dulles International Airport that serves multiple airline gates and amenities within the airport’s secure 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_69f34962386c81909ddc3bf9e18ddeb8 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6db1f3ec48190a82e7d893d3c76ba completed May 3, 2026, 5:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3576d3b0488190acced2c2b89eec66 completed June 19, 2026, 5:05 p.m.
NEDg Description generation batch_6a357780ccf88190bf11e9d5de9b3025 completed June 19, 2026, 5:08 p.m.
NED2 Entity disambiguation (via description) batch_6a35788be1508190b1794f59d4c78502 completed June 19, 2026, 5:12 p.m.
Created at: May 1, 2026, 1:31 a.m.