Triple

T10396006
Position Surface form Disambiguated ID Type / Status
Subject Suite from 'Ma Ma' E245019 entity
Predicate basedOnFilm P15523 FINISHED
Object Ma Ma
Ma Ma is a 2015 Spanish drama film directed by Julio Medem and starring Penélope Cruz as a woman confronting a breast cancer diagnosis while rebuilding her life.
 E860429 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: Ma Ma | Statement: [Suite from 'Ma Ma', basedOnFilm, Ma Ma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ma Ma
Context triple: [Suite from 'Ma Ma', basedOnFilm, Ma Ma]
  • A. Ma-Ma
    Ma-Ma is the ruthless, scarred gang leader and primary antagonist in the 2012 science fiction action film "Dredd."
  • B. MAMA
    MAMA is the commonly used acronym for the MTV Africa Music Awards, an annual event celebrating contemporary African music and artists.
  • C. MAMA
    MAMA (Murray Art Museum Albury) is a contemporary art museum and cultural venue located in Albury, New South Wales, Australia.
  • D. La MaMa
    La MaMa is a renowned Off-Off-Broadway experimental theater in New York City known for fostering avant-garde performance and emerging artists.
  • E. Mama
    "Mama" is a 1987 debut novel by Terry McMillan that follows a resilient Black single mother struggling to raise her children and rebuild her life amid poverty and personal turmoil.
  • 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: Ma Ma
Triple: [Suite from 'Ma Ma', basedOnFilm, Ma Ma]
Generated description
Ma Ma is a 2015 Spanish drama film directed by Julio Medem and starring Penélope Cruz as a woman confronting a breast cancer diagnosis while rebuilding her life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ma Ma
Target entity description: Ma Ma is a 2015 Spanish drama film directed by Julio Medem and starring Penélope Cruz as a woman confronting a breast cancer diagnosis while rebuilding her life.
  • A. Ma-Ma
    Ma-Ma is the ruthless, scarred gang leader and primary antagonist in the 2012 science fiction action film "Dredd."
  • B. MAMA
    MAMA (Murray Art Museum Albury) is a contemporary art museum and cultural venue located in Albury, New South Wales, Australia.
  • C. MAMA
    MAMA is the commonly used acronym for the MTV Africa Music Awards, an annual event celebrating contemporary African music and artists.
  • D. La MaMa
    La MaMa is a renowned Off-Off-Broadway experimental theater in New York City known for fostering avant-garde performance and emerging artists.
  • E. Mama
    "Mama" is a 1987 debut novel by Terry McMillan that follows a resilient Black single mother struggling to raise her children and rebuild her life amid poverty and personal turmoil.
  • 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_69d381b5116081908d85227bab6d3c0c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9cf79348190975d6c1791e3b621 completed April 7, 2026, 11:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69d795cf331c8190b35caf3997dc29a3 completed April 9, 2026, 12:04 p.m.
NEDg Description generation batch_69d7bde050ac8190b87a0c81700ad1b1 completed April 9, 2026, 2:55 p.m.
NED2 Entity disambiguation (via description) batch_69d7e60afaf481909a0790c94e323143 completed April 9, 2026, 5:46 p.m.
Created at: April 6, 2026, 12:06 p.m.