Triple

T1318327
Position Surface form Disambiguated ID Type / Status
Subject Yann Arthus-Bertrand E28157 entity
Predicate notableWork P4 FINISHED
Object Human
"Human" is a 2015 documentary film by Yann Arthus-Bertrand that weaves together intimate interviews and sweeping aerial imagery to explore the shared experiences, emotions, and challenges of people around the world.
 E150413 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: Human | Statement: [Yann Arthus-Bertrand, notableWork, Human]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Human
Context triple: [Yann Arthus-Bertrand, notableWork, Human]
  • A. MAN
    MAN is the three-letter IATA airport code for Manchester Airport, a major international airport serving the Greater Manchester area in England.
  • B. MAN
    MAN is a German commercial vehicle and engineering company best known for manufacturing trucks, buses, and diesel engines.
  • C. אָדָם
    אָדָם is the Hebrew name for Adam, the first human in the Biblical creation narrative and a foundational figure in Jewish, Christian, and Islamic traditions.
  • D. Mann
    Mann is a common German surname borne by numerous notable figures in literature, politics, and the arts.
  • E. He
    He is a common Chinese surname borne by numerous notable figures across politics, military, arts, and academia.
  • 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: Human
Triple: [Yann Arthus-Bertrand, notableWork, Human]
Generated description
"Human" is a 2015 documentary film by Yann Arthus-Bertrand that weaves together intimate interviews and sweeping aerial imagery to explore the shared experiences, emotions, and challenges of people around the world.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Human
Target entity description: "Human" is a 2015 documentary film by Yann Arthus-Bertrand that weaves together intimate interviews and sweeping aerial imagery to explore the shared experiences, emotions, and challenges of people around the world.
  • A. MAN
    MAN is the three-letter IATA airport code for Manchester Airport, a major international airport serving the Greater Manchester area in England.
  • B. MAN
    MAN is a German commercial vehicle and engineering company best known for manufacturing trucks, buses, and diesel engines.
  • C. אָדָם
    אָדָם is the Hebrew name for Adam, the first human in the Biblical creation narrative and a foundational figure in Jewish, Christian, and Islamic traditions.
  • D. Mann
    Mann is a common German surname borne by numerous notable figures in literature, politics, and the arts.
  • E. He
    He is a common Chinese surname borne by numerous notable figures across politics, military, arts, and academia.
  • 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_69a498532c3481909223b74af2e578df completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c176c89881909e9dc0e34f12f056 completed March 1, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbaf3cef88190ab1635bc5f452f8b completed March 7, 2026, 11:55 p.m.
NEDg Description generation batch_69acbb5ee684819083f5309dc9771c3a completed March 7, 2026, 11:57 p.m.
NED2 Entity disambiguation (via description) batch_69acbc010cd0819080b1f8695dc0990b completed March 8, 2026, midnight
Created at: March 1, 2026, 7:55 p.m.