Triple

T31003881
Position Surface form Disambiguated ID Type / Status
Subject Autair International Airways E790011 entity
Predicate fleetType P6198 FINISHED
Object de Havilland Dove
The de Havilland Dove is a British short-haul, twin-engine passenger aircraft introduced in the late 1940s and widely used by regional airlines and military operators around the world.
 E1942764 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: de Havilland Dove | Statement: [Autair International Airways, fleetType, de Havilland Dove]
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: de Havilland Dove
Triple: [Autair International Airways, fleetType, de Havilland Dove]
Generated description
The de Havilland Dove is a British short-haul, twin-engine passenger aircraft introduced in the late 1940s and widely used by regional airlines and military operators around the world.

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_69f224c73ca48190a1e46cb58ad4045b completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6944331808190a5c5b3b30c8d3a7e completed May 3, 2026, 12:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29183441a881909b5d2da676433345 completed June 10, 2026, 7:54 a.m.
NEDg Description generation batch_6a2918f4ee448190baf0697c0a4bac1c completed June 10, 2026, 7:57 a.m.
NED2 Entity disambiguation (via description) batch_6a291bbf5db88190b416bbf549343a86 completed June 10, 2026, 8:09 a.m.
Created at: April 29, 2026, 8:57 p.m.