Triple

T28209146
Position Surface form Disambiguated ID Type / Status
Subject Radioplane Company E717109 entity
Predicate product P490 FINISHED
Object Radioplane OQ-19
The Radioplane OQ-19 was a widely used American target drone developed in the mid-20th century for training anti-aircraft gunners and testing air defense systems.
 E1819426 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: Radioplane OQ-19 | Statement: [Radioplane Company, product, Radioplane OQ-19]
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: Radioplane OQ-19
Triple: [Radioplane Company, product, Radioplane OQ-19]
Generated description
The Radioplane OQ-19 was a widely used American target drone developed in the mid-20th century for training anti-aircraft gunners and testing air defense systems.

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_69efd6b826908190857e6e7dad74ed93 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f64310b67481908c2de1bc652d4d87 completed May 2, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a164165f558819083b02e7ed2592c5b completed May 27, 2026, 12:57 a.m.
NEDg Description generation batch_6a1642a04a9c81908f196894b8f4bdf5 completed May 27, 2026, 1:02 a.m.
NED2 Entity disambiguation (via description) batch_6a164322f1148190b37794a5fc54f184 completed May 27, 2026, 1:04 a.m.
Created at: April 27, 2026, 10:37 p.m.