Triple

T27464301
Position Surface form Disambiguated ID Type / Status
Subject Mulsanne Straight E693133 entity
Predicate circuitSectionOrder P15216 FINISHED
Object follows Tertre Rouge on a lap of Le Mans
Mulsanne Straight is the famously long, high-speed straight section of the Circuit de la Sarthe used in the 24 Hours of Le Mans endurance race.
 E1775571 NE FINISHED

How this triple was built (3 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: follows Tertre Rouge on a lap of Le Mans | Statement: [Mulsanne Straight, circuitSectionOrder, follows Tertre Rouge on a lap of Le Mans]
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: follows Tertre Rouge on a lap of Le Mans
Triple: [Mulsanne Straight, circuitSectionOrder, follows Tertre Rouge on a lap of Le Mans]
Generated description
Mulsanne Straight is the famously long, high-speed straight section of the Circuit de la Sarthe used in the 24 Hours of Le Mans endurance race.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: circuitSectionOrder
Context triple: [Mulsanne Straight, circuitSectionOrder, follows Tertre Rouge on a lap of Le Mans]
  • A. circuitSection
    Indicates that one entity is a specific section or segment within a larger circuit.
  • B. circuitDirection
    Indicates the orientation or flow direction of a circuit or circuit path relative to its components or layout.
  • C. belongsToCircuitNumbering
    Indicates that an entity is associated with, or assigned to, a specific circuit numbering scheme or identifier.
  • D. componentOrder chosen
    Indicates the relative sequence or arrangement of components within a larger structure or system.
  • E. confersOrder
    Indicates that one entity formally bestows or grants an order, rank, or honorific distinction upon another entity.
  • F. None of above.

Provenance (6 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_69ef538105548190a771cc5a0cf8c211 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62dfbe6508190a6871084c5afe20f completed May 2, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbdcabbc8190b26943ef425d7397 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bcf8cd9c81909f9f001e8a1d4a81 completed May 24, 2026, 8:55 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd75c610819081ae1b4f7fedb4cb completed May 24, 2026, 8:57 a.m.
PD Predicate disambiguation batch_69f623aaf40081909f947431424a1d55 completed May 2, 2026, 4:17 p.m.
Created at: April 27, 2026, 12:51 p.m.