Triple

T36348399

Position	Surface form	Disambiguated ID	Type / Status
Subject	Fábrica Militar de Aviones	`E895127`	entity
Predicate	notableAircraftDesigned	`P204832`	FINISHED
Object	FMA IAe 22 DL The FMA IAe 22 DL was an Argentine advanced trainer and light attack aircraft developed in the 1940s as part of the country’s early indigenous military aviation efforts.	`E2138642`	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: FMA IAe 22 DL | Statement: [Fábrica Militar de Aviones, notableAircraftDesigned, FMA IAe 22 DL]

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: FMA IAe 22 DL
Triple: [Fábrica Militar de Aviones, notableAircraftDesigned, FMA IAe 22 DL]

Generated description

The FMA IAe 22 DL was an Argentine advanced trainer and light attack aircraft developed in the 1940s as part of the country’s early indigenous military aviation efforts.

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_69f76e4f437c8190a1af3ea2564f41f5`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_6a03809bd57c8190beb371feaf44a7db`	completed	May 12, 2026, 7:33 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a39a327b1a08190b92ca04777088972`	completed	June 22, 2026, 9:03 p.m.
NEDg	Description generation	`batch_6a39a40c7d908190b5981106babcfbb7`	completed	June 22, 2026, 9:07 p.m.
NED2	Entity disambiguation (via description)	`batch_6a39a6b290148190b8e4649efd67a390`	completed	June 22, 2026, 9:18 p.m.

Created at: May 3, 2026, 4:09 p.m.