Triple

T3298571
Position Surface form Disambiguated ID Type / Status
Subject Federico Luppi E69274 entity
Predicate notableWork P4 FINISHED
Object Martín (Hache)
Martín (Hache) is a 1997 Argentine-Spanish drama film that explores generational conflict, identity, and disillusionment through the strained relationship between a troubled young man and his estranged father in Madrid.
 E347511 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: Martín (Hache) | Statement: [Federico Luppi, notableWork, Martín (Hache)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martín (Hache)
Context triple: [Federico Luppi, notableWork, Martín (Hache)]
  • A. Martín
    Martín is a masculine given name of Latin origin, commonly used in Spanish-speaking countries and derived from the name Martinus, associated with the Roman god Mars.
  • B. Herrero
    Herrero is a Spanish occupational surname derived from the word for "blacksmith" or "smith."
  • C. Martínez
    Martínez is a common Spanish-language surname widely borne across Spain and Latin America.
  • D. Sebastián
    Sebastián is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
  • E. Magaña
    Magaña is a Spanish-language surname of Hispanic origin borne by various notable individuals in Mexico and other Spanish-speaking countries.
  • 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: Martín (Hache)
Triple: [Federico Luppi, notableWork, Martín (Hache)]
Generated description
Martín (Hache) is a 1997 Argentine-Spanish drama film that explores generational conflict, identity, and disillusionment through the strained relationship between a troubled young man and his estranged father in Madrid.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martín (Hache)
Target entity description: Martín (Hache) is a 1997 Argentine-Spanish drama film that explores generational conflict, identity, and disillusionment through the strained relationship between a troubled young man and his estranged father in Madrid.
  • A. Martín
    Martín is a masculine given name of Latin origin, commonly used in Spanish-speaking countries and derived from the name Martinus, associated with the Roman god Mars.
  • B. Herrero
    Herrero is a Spanish occupational surname derived from the word for "blacksmith" or "smith."
  • C. Martínez
    Martínez is a common Spanish-language surname widely borne across Spain and Latin America.
  • D. Sebastián
    Sebastián is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
  • E. Magaña
    Magaña is a Spanish-language surname of Hispanic origin borne by various notable individuals in Mexico and other Spanish-speaking countries.
  • 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_69ad859e529c8190a404273f53cb487d completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0a49b748190b6db99a85c3cb3c5 completed March 8, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3d759908190b1f5170930ff03c5 completed March 12, 2026, 5:11 p.m.
NEDg Description generation batch_69b2f9ec098c8190aaa763d7b9c5cceb completed March 12, 2026, 5:37 p.m.
NED2 Entity disambiguation (via description) batch_69b316408090819090a4792b3d5185f0 completed March 12, 2026, 7:38 p.m.
Created at: March 8, 2026, 3:10 p.m.