Triple

T4101094
Position Surface form Disambiguated ID Type / Status
Subject Mudbound E87941 entity
Predicate composer P1361 FINISHED
Object Tamar-kali
Tamar-kali is an American composer, singer-songwriter, and guitarist known for her genre-blending work and acclaimed film scores, including her breakthrough on the period drama "Mudbound."
E414069 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: Tamar-kali | Statement: [Mudbound, composer, Tamar-kali]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tamar-kali
Context triple: [Mudbound, composer, Tamar-kali]
  • A. Tamalu
    Tamalu is a village located on Car Nicobar Island in the Nicobar district of India’s Andaman and Nicobar Islands.
  • B. Tamambo
    Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
  • C. Taroombal
    Taroombal is a clan of the Darumbal Aboriginal people, a First Nations group traditionally associated with the Rockhampton region of central Queensland, Australia.
  • D. Sawanih
    Sawanih is a notable literary work by the Indian poet Faizi, recognized for its contribution to classical Persian literature in South Asia.
  • E. Yambu
    Yambu is a coastal city in western Saudi Arabia on the Red Sea, known as an important port and industrial center.
  • 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: Tamar-kali
Triple: [Mudbound, composer, Tamar-kali]
Generated description
Tamar-kali is an American composer, singer-songwriter, and guitarist known for her genre-blending work and acclaimed film scores, including her breakthrough on the period drama "Mudbound."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tamar-kali
Target entity description: Tamar-kali is an American composer, singer-songwriter, and guitarist known for her genre-blending work and acclaimed film scores, including her breakthrough on the period drama "Mudbound."
  • A. Tamalu
    Tamalu is a village located on Car Nicobar Island in the Nicobar district of India’s Andaman and Nicobar Islands.
  • B. Tamambo
    Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
  • C. Taroombal
    Taroombal is a clan of the Darumbal Aboriginal people, a First Nations group traditionally associated with the Rockhampton region of central Queensland, Australia.
  • D. Sawanih
    Sawanih is a notable literary work by the Indian poet Faizi, recognized for its contribution to classical Persian literature in South Asia.
  • E. Yambu
    Yambu is a coastal city in western Saudi Arabia on the Red Sea, known as an important port and industrial center.
  • 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_69aed94564cc8190a9c1457daedb6e7f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefd0ed168819093c83ba079d6725c completed March 9, 2026, 5:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b7833b081909bf5a87ee709b49f completed March 14, 2026, 2:06 p.m.
NEDg Description generation batch_69b56f33f7688190a017e4486a3ba542 completed March 14, 2026, 2:22 p.m.
NED2 Entity disambiguation (via description) batch_69b56fafb8208190997b6d91c4a1774d completed March 14, 2026, 2:24 p.m.
Created at: March 9, 2026, 3:40 p.m.