Triple

T27090561
Position Surface form Disambiguated ID Type / Status
Subject Dempsey Racing E686154 entity
Predicate hasChassis P7999 FINISHED
Object Porsche 911 GT America
The Porsche 911 GT America is a race-prepared variant of the Porsche 911, developed specifically for North American GT competition with performance and durability upgrades over the road-going model.
 E1764325 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 GT America | Statement: [Dempsey Racing, hasChassis, Porsche 911 GT America]
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 GT America
Triple: [Dempsey Racing, hasChassis, Porsche 911 GT America]
Generated description
The Porsche 911 GT America is a race-prepared variant of the Porsche 911, developed specifically for North American GT competition with performance and durability upgrades over the road-going model.

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_69ef148940ec819097b5c20fbfbf7c81 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f62349888081908305958558907967 completed May 2, 2026, 4:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a126258a8008190bc16a98ca24fae02 completed May 24, 2026, 2:28 a.m.
NEDg Description generation batch_6a126eb9c5e481908c55443be308661f completed May 24, 2026, 3:21 a.m.
NED2 Entity disambiguation (via description) batch_6a126f302344819088f9e40ef39d8f58 completed May 24, 2026, 3:23 a.m.
Created at: April 27, 2026, 8:40 a.m.