Triple

T1116548
Position Surface form Disambiguated ID Type / Status
Subject Tobin Heath E11113 entity
Predicate givenName P17 FINISHED
Object Tobin
Tobin is the given name of Tobin Heath, an American professional soccer player and multiple-time FIFA Women's World Cup champion.
 E129018 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: Tobin | Statement: [Tobin Heath, givenName, Tobin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tobin
Context triple: [Tobin Heath, givenName, Tobin]
  • A. Roth
    Roth is a locality within the German town of Lichtenfels in the state of Bavaria.
  • B. Roth
    Roth is a town in the Bavarian region of Middle Franconia in southern Germany.
  • C. Minsky
    Minsky is a surname most notably associated with Marvin Minsky, a pioneering American cognitive scientist and co-founder of the field of artificial intelligence.
  • D. Rubin
    Rubin is a surname most famously associated with American astronomer Vera Rubin, whose work on galaxy rotation curves provided key evidence for the existence of dark matter.
  • E. Tucker
    Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
  • 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: Tobin
Triple: [Tobin Heath, givenName, Tobin]
Generated description
Tobin is the given name of Tobin Heath, an American professional soccer player and multiple-time FIFA Women's World Cup champion.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tobin
Target entity description: Tobin is the given name of Tobin Heath, an American professional soccer player and multiple-time FIFA Women's World Cup champion.
  • A. Roth
    Roth is a locality within the German town of Lichtenfels in the state of Bavaria.
  • B. Roth
    Roth is a town in the Bavarian region of Middle Franconia in southern Germany.
  • C. Minsky
    Minsky is a surname most notably associated with Marvin Minsky, a pioneering American cognitive scientist and co-founder of the field of artificial intelligence.
  • D. Rubin
    Rubin is a surname most famously associated with American astronomer Vera Rubin, whose work on galaxy rotation curves provided key evidence for the existence of dark matter.
  • E. Tucker
    Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
  • 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_69a493252a648190ac48f8742474a5e8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4bba2b550819081f8a100638d2fba completed March 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac53999b3c8190aff1cf84a3c16909 completed March 7, 2026, 4:34 p.m.
NEDg Description generation batch_69ac554aec048190821801070d1a4852 completed March 7, 2026, 4:41 p.m.
NED2 Entity disambiguation (via description) batch_69ac55afd8c88190b0f2bbafc33ad8b7 completed March 7, 2026, 4:43 p.m.
Created at: March 1, 2026, 7:43 p.m.