Triple

T9937327
Position Surface form Disambiguated ID Type / Status
Subject In the Land of Blood and Honey E193989 entity
Predicate mainCharacter P1183 FINISHED
Object Danijel
Danijel is the central male protagonist in the war drama film "In the Land of Blood and Honey," which explores a complex relationship set against the backdrop of the Bosnian War.
 E832842 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: Danijel | Statement: [In the Land of Blood and Honey, mainCharacter, Danijel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Danijel
Context triple: [In the Land of Blood and Honey, mainCharacter, Danijel]
  • A. Matija
    Matija is a South Slavic given name, equivalent to the English name Matthew.
  • B. Vlatko
    Vlatko is a masculine given name commonly used in Slavic countries, particularly in North Macedonia and other parts of the Balkans.
  • C. Saša
    Saša is a given name commonly used in Slavic countries, often as a diminutive of Aleksandar or Aleksandra.
  • D. Ilija
    Ilija is a masculine given name of Slavic origin, commonly used in countries such as Bulgaria, Serbia, and North Macedonia.
  • E. Željko
    Željko is a masculine given name of South Slavic origin, commonly used in countries such as Croatia and Slovenia.
  • 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: Danijel
Triple: [In the Land of Blood and Honey, mainCharacter, Danijel]
Generated description
Danijel is the central male protagonist in the war drama film "In the Land of Blood and Honey," which explores a complex relationship set against the backdrop of the Bosnian War.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Danijel
Target entity description: Danijel is the central male protagonist in the war drama film "In the Land of Blood and Honey," which explores a complex relationship set against the backdrop of the Bosnian War.
  • A. Matija
    Matija is a South Slavic given name, equivalent to the English name Matthew.
  • B. Vlatko
    Vlatko is a masculine given name commonly used in Slavic countries, particularly in North Macedonia and other parts of the Balkans.
  • C. Saša
    Saša is a given name commonly used in Slavic countries, often as a diminutive of Aleksandar or Aleksandra.
  • D. Ilija
    Ilija is a masculine given name of Slavic origin, commonly used in countries such as Bulgaria, Serbia, and North Macedonia.
  • E. Željko
    Željko is a masculine given name of South Slavic origin, commonly used in countries such as Croatia and Slovenia.
  • 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_69ca82e409348190a393777356b80a2a completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5e4e19881909879b394090d6629 completed April 2, 2026, 12:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69d23d4528108190b38111bb36832a67 completed April 5, 2026, 10:45 a.m.
NEDg Description generation batch_69d23eb1c1f481908404225dcccd0697 completed April 5, 2026, 10:51 a.m.
NED2 Entity disambiguation (via description) batch_69d242aea6a08190a73a836e59865c35 completed April 5, 2026, 11:08 a.m.
Created at: March 30, 2026, 8:44 p.m.