Triple

T29232860

Position	Surface form	Disambiguated ID	Type / Status
Subject	Toyota Celica	`E741116`	entity
Predicate	predecessor	`P97`	FINISHED
Object	Toyota 1600GT The Toyota 1600GT is a late-1960s high-performance sports coupe derived from the Corona lineup, notable as one of Toyota’s early GT models that helped pave the way for later sporty cars like the Celica.	`E1855600`	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: Toyota 1600GT | Statement: [Toyota Celica, predecessor, Toyota 1600GT]

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: Toyota 1600GT
Triple: [Toyota Celica, predecessor, Toyota 1600GT]

Generated description

The Toyota 1600GT is a late-1960s high-performance sports coupe derived from the Corona lineup, notable as one of Toyota’s early GT models that helped pave the way for later sporty cars like the Celica.

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_69f0911dd6fc819097d1abb287016489`	completed	April 28, 2026, 10:51 a.m.
NER	Named-entity recognition	`batch_69f6646167dc819085194ef9f5d96d23`	completed	May 2, 2026, 8:53 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a2569dba9348190bb4e34470aab3aca`	completed	June 7, 2026, 12:53 p.m.
NEDg	Description generation	`batch_6a256e3f248c819090c3d806f3c3fd84`	completed	June 7, 2026, 1:12 p.m.
NED2	Entity disambiguation (via description)	`batch_6a2572394c84819085d3812520aeb050`	completed	June 7, 2026, 1:29 p.m.

Created at: April 28, 2026, 12:27 p.m.