Triple

T143830
Position Surface form Disambiguated ID Type / Status
Subject Yann LeCun E2909 entity
Predicate givenName P17 FINISHED
Object Yann
Yann is the given name of Yann LeCun, a pioneering computer scientist known for his foundational work in deep learning and convolutional neural networks.
 E26462 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: Yann | Statement: [Yann LeCun, givenName, Yann]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yann
Context triple: [Yann LeCun, givenName, Yann]
  • A. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • B. Georges
    Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
  • C. Pierre
    Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
  • D. André
    André is a given name of French origin commonly used in various languages as a form of "Andrew."
  • E. Bernard
    Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
  • 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: Yann
Triple: [Yann LeCun, givenName, Yann]
Generated description
Yann is the given name of Yann LeCun, a pioneering computer scientist known for his foundational work in deep learning and convolutional neural networks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yann
Target entity description: Yann is the given name of Yann LeCun, a pioneering computer scientist known for his foundational work in deep learning and convolutional neural networks.
  • A. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • B. Georges
    Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
  • C. Pierre
    Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
  • D. André
    André is a given name of French origin commonly used in various languages as a form of "Andrew."
  • E. Bernard
    Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
  • 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_69a2521e35c08190b28e5c9f1e3c9b59 completed Feb. 28, 2026, 2:25 a.m.
NER Named-entity recognition batch_69a257caf678819092e975d5167f9df4 completed Feb. 28, 2026, 2:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69a32f269a508190835d587125342870 completed Feb. 28, 2026, 6:08 p.m.
NEDg Description generation batch_69a32f8e6964819089360153a79b0772 completed Feb. 28, 2026, 6:10 p.m.
NED2 Entity disambiguation (via description) batch_69a32fdb96f48190b2810f06a694fbba completed Feb. 28, 2026, 6:11 p.m.
Created at: Feb. 28, 2026, 2:31 a.m.