Triple

T11713483
Position Surface form Disambiguated ID Type / Status
Subject Princesse Tam-Tam E278429 entity
Predicate character P662 FINISHED
Object Dar
Dar is a character from the 1935 French film "Princesse Tam-Tam," which starred Josephine Baker.
 E942077 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: Dar | Statement: [Princesse Tam-Tam, character, Dar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dar
Context triple: [Princesse Tam-Tam, character, Dar]
  • A. Dar
    Dar is the warrior protagonist and titular Beastmaster of the Beastmaster fantasy franchise, known for his ability to telepathically communicate with and command animals.
  • B. Dal
    Dal is a village and railway station in Eidsvoll municipality in Norway, serving as a terminus for some Oslo commuter rail services.
  • C. Dal
    Dal is the commonly used short form for Dalhousie University, a major public research university in Halifax, Nova Scotia, Canada.
  • D. Der
    Der was an ancient Mesopotamian city known as an important religious center associated with the worship of the god Anu.
  • E. Des
    Des is a given name, typically used as a shortened form of Desmond.
  • 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: Dar
Triple: [Princesse Tam-Tam, character, Dar]
Generated description
Dar is a character from the 1935 French film "Princesse Tam-Tam," which starred Josephine Baker.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dar
Target entity description: Dar is a character from the 1935 French film "Princesse Tam-Tam," which starred Josephine Baker.
  • A. Dar
    Dar is the warrior protagonist and titular Beastmaster of the Beastmaster fantasy franchise, known for his ability to telepathically communicate with and command animals.
  • B. Dal
    Dal is the commonly used short form for Dalhousie University, a major public research university in Halifax, Nova Scotia, Canada.
  • C. Dal
    Dal is a village and railway station in Eidsvoll municipality in Norway, serving as a terminus for some Oslo commuter rail services.
  • D. Der
    Der was an ancient Mesopotamian city known as an important religious center associated with the worship of the god Anu.
  • E. Des
    Des is a given name, typically used as a shortened form of Desmond.
  • 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4be10088190854699385d1f6a95 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef838562d08190b9a764e88c50d423 completed April 27, 2026, 3:40 p.m.
NEDg Description generation batch_69ef9b68309081909f3f614efeeb2ab1 completed April 27, 2026, 5:22 p.m.
NED2 Entity disambiguation (via description) batch_69efd6aba82c81909ff22e6b26db3cfe completed April 27, 2026, 9:35 p.m.
Created at: April 8, 2026, 9:40 p.m.