Triple

T19689810
Position Surface form Disambiguated ID Type / Status
Subject Italian Grand Prix E472803 entity
Predicate corners P42380 FINISHED
Object Lesmo
Lesmo is a pair of fast right-hand corners at Italy’s Monza circuit, renowned as one of the most challenging and iconic sections of the Formula 1 calendar.
 E1390022 NE FINISHED

How this triple was built (4 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: Lesmo | Statement: [Italian Grand Prix, corners, Lesmo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lesmo
Context triple: [Italian Grand Prix, corners, Lesmo]
  • A. Cheux
    Cheux is a small commune in the Calvados department of the Normandy region in northwestern France.
  • B. Lombe
    Lombe is a city located in the Indonesian province of Southeast Sulawesi.
  • C. Lepechin
    Lepechin was an 18th-century Russian naturalist and explorer known for his extensive zoological and botanical studies across the Russian Empire.
  • D. Champoz
    Champoz is a small Swiss municipality and village located in the French-speaking Bernese Jura region.
  • E. Lezayr
    Lezayr is a parish on the Isle of Man, known for its rural landscapes and historic churches.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Lesmo
Triple: [Italian Grand Prix, corners, Lesmo]
Generated description
Lesmo is a pair of fast right-hand corners at Italy’s Monza circuit, renowned as one of the most challenging and iconic sections of the Formula 1 calendar.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lesmo
Target entity description: Lesmo is a pair of fast right-hand corners at Italy’s Monza circuit, renowned as one of the most challenging and iconic sections of the Formula 1 calendar.
  • A. Cheux
    Cheux is a small commune in the Calvados department of the Normandy region in northwestern France.
  • B. Lombe
    Lombe is a city located in the Indonesian province of Southeast Sulawesi.
  • C. Lepechin
    Lepechin was an 18th-century Russian naturalist and explorer known for his extensive zoological and botanical studies across the Russian Empire.
  • D. Champoz
    Champoz is a small Swiss municipality and village located in the French-speaking Bernese Jura region.
  • E. Lezayr
    Lezayr is a parish on the Isle of Man, known for its rural landscapes and historic churches.
  • F. None of above. chosen

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_69d8e515bef88190bc30781aea50537a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6420f1a0c8190ae59aa0ab3ff2802 completed April 20, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0787cce7248190bbec313b04d7a64e completed May 15, 2026, 8:53 p.m.
NEDg Description generation batch_6a078a9e163481908db5749a53b13305 completed May 15, 2026, 9:05 p.m.
NED2 Entity disambiguation (via description) batch_6a078b0c6ae88190a8a17a7c1381ae41 completed May 15, 2026, 9:07 p.m.
Created at: April 10, 2026, 1:45 p.m.