Triple

T24791813

Position	Surface form	Disambiguated ID	Type / Status
Subject	GT Le Mans	`E620269`	entity
Predicate	notableModel	`P1503`	FINISHED
Object	Porsche 911 RSR The Porsche 911 RSR is a factory-built, race-spec version of the 911 sports car designed for top-level endurance racing, including events like the 24 Hours of Le Mans.	`E1654847`	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: Porsche 911 RSR | Statement: [GT Le Mans, notableModel, Porsche 911 RSR]

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: Porsche 911 RSR
Triple: [GT Le Mans, notableModel, Porsche 911 RSR]

Generated description

The Porsche 911 RSR is a factory-built, race-spec version of the 911 sports car designed for top-level endurance racing, including events like the 24 Hours of Le Mans.

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_69e2fabe77c8819085f7ce6486248139`	completed	April 18, 2026, 3:30 a.m.
NER	Named-entity recognition	`batch_69f4110424188190889a13976b16b18a`	completed	May 1, 2026, 2:33 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a101c2a84f48190a0a5aa0d0da7e568`	completed	May 22, 2026, 9:04 a.m.
NEDg	Description generation	`batch_6a10279326b48190927cdfc7ac0e1790`	completed	May 22, 2026, 9:53 a.m.
NED2	Entity disambiguation (via description)	`batch_6a102831042c8190a71800f81513ddbf`	completed	May 22, 2026, 9:56 a.m.

Created at: April 18, 2026, 4:47 a.m.