Triple

T32449458
Position Surface form Disambiguated ID Type / Status
Subject P-35 E829237 entity
Predicate manufacturer P490 FINISHED
Object Seversky Aircraft Company
Seversky Aircraft Company was an American aviation firm of the 1930s known for pioneering all-metal, single-seat fighter aircraft that helped lay the groundwork for later World War II fighters.
 E232721 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: Seversky Aircraft Company | Statement: [P-35, manufacturer, Seversky Aircraft Company]
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: Seversky Aircraft Company
Triple: [P-35, manufacturer, Seversky Aircraft Company]
Generated description
Seversky Aircraft Company was an American aviation firm of the 1930s known for pioneering all-metal, single-seat fighter aircraft that helped lay the groundwork for later World War II fighters.

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_69f3491d2e5c819092b1c9535beff8ec completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c2e9377881909543960edd137c77 completed May 3, 2026, 3:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a347b70d8a0819097a2b63d6a786275 completed June 18, 2026, 11:12 p.m.
NEDg Description generation batch_6a347c67712081908c1641c46b1abc0c completed June 18, 2026, 11:16 p.m.
NED2 Entity disambiguation (via description) batch_6a347cebd4f08190856060b0b27e2186 completed June 18, 2026, 11:19 p.m.
Created at: May 1, 2026, 12:56 a.m.