Triple

T27642931
Position Surface form Disambiguated ID Type / Status
Subject Garmin-Slipstream E696632 entity
Predicate hadRider P59648 FINISHED
Object Tyler Farrar
Tyler Farrar is an American former professional road cyclist known as a top sprinter, with stage wins in Grand Tours such as the Vuelta a España and the Tour de France.
 E1782661 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: Tyler Farrar | Statement: [Garmin-Slipstream, hadRider, Tyler Farrar]
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: Tyler Farrar
Triple: [Garmin-Slipstream, hadRider, Tyler Farrar]
Generated description
Tyler Farrar is an American former professional road cyclist known as a top sprinter, with stage wins in Grand Tours such as the Vuelta a España and the Tour de France.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hadRider
Context triple: [Garmin-Slipstream, hadRider, Tyler Farrar]
  • A. hadFort
    Indicates that an entity possessed, controlled, or contained a fort at some time.
  • B. laterRider
    Indicates that one rider or passenger occurs or appears later in time than another rider or passenger within a given context or sequence.
  • C. hasOwnerRider
    Indicates that an entity (such as an animal or vehicle) has a specific person who both owns it and rides or uses it.
  • D. isRiddenBy chosen
    Indicates that an entity serves as a mount or vehicle that is being ridden by another entity.
  • E. riderType
    Indicates the category or role of a rider in relation to a ride, transport service, or vehicle (e.g., passenger, driver, courier).
  • 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_69ef5909f3848190805f35b76833e722 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f643ed0b7481908cf25f3afec0a61d completed May 2, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12da9207b081909002abf97f09a6b4 completed May 24, 2026, 11:01 a.m.
NEDg Description generation batch_6a12db41934c8190b860473fb4b6c979 completed May 24, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a12dbcfd4588190a6b414466e5bc7cb completed May 24, 2026, 11:06 a.m.
PD Predicate disambiguation batch_69f641dc8ff48190ab575d855616580c completed May 2, 2026, 6:26 p.m.
Created at: April 27, 2026, 2:27 p.m.