Triple

T33819922
Position Surface form Disambiguated ID Type / Status
Subject Farman HF.20 E866798 entity
Predicate successor P78 FINISHED
Object Farman HF.23
The Farman HF.23 was an early French military biplane developed during the 1910s as part of the Farman series of reconnaissance and training aircraft.
 E2092245 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: Farman HF.23 | Statement: [Farman HF.20, successor, Farman HF.23]
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: Farman HF.23
Triple: [Farman HF.20, successor, Farman HF.23]
Generated description
The Farman HF.23 was an early French military biplane developed during the 1910s as part of the Farman series of reconnaissance and training aircraft.

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_69f349911a8c81908478662194b23d8c completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6fffb87108190b3e02be4678bdc73 completed May 3, 2026, 7:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36f9aa31d4819083078cbef789b200 completed June 20, 2026, 8:35 p.m.
NEDg Description generation batch_6a36fa7cf07c819080d18e1c419568f7 completed June 20, 2026, 8:39 p.m.
NED2 Entity disambiguation (via description) batch_6a36fe233bb8819096419ecbfeebc79b completed June 20, 2026, 8:54 p.m.
Created at: May 1, 2026, 1:46 a.m.