Triple

T22741673
Position Surface form Disambiguated ID Type / Status
Subject Homefront E562429 entity
Predicate mainCharacter P1183 FINISHED
Object Al Kahn
Al Kahn is the protagonist of the film "Homefront," around whom the story’s central conflicts and personal struggles revolve.
 E1551985 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: Al Kahn | Statement: [Homefront, mainCharacter, Al Kahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Al Kahn
Context triple: [Homefront, mainCharacter, Al Kahn]
  • A. Khan Arnabah
    Khan Arnabah is a town in southern Syria located in the Golan Heights region near the city of Quneitra.
  • B. George Khan
    George Khan is the strict, traditional Pakistani-British patriarch in the film "East Is East," whose attempts to impose his cultural values on his mixed-heritage family drive the story's central conflict.
  • C. Milini Khan
    Milini Khan is an American singer and the daughter of renowned R&B and funk vocalist Chaka Khan.
  • D. Khan Dauran
    Khan Dauran was a prominent Mughal military commander and nobleman of the early 18th century who played a key role in the empire’s defense during Nader Shah’s invasion of India.
  • E. Tauke Khan
    Tauke Khan was a prominent 17th–18th century Kazakh ruler known for unifying the Kazakh tribes and codifying traditional laws into the "Jety Jargy" legal code.
  • 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: Al Kahn
Triple: [Homefront, mainCharacter, Al Kahn]
Generated description
Al Kahn is the protagonist of the film "Homefront," around whom the story’s central conflicts and personal struggles revolve.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Al Kahn
Target entity description: Al Kahn is the protagonist of the film "Homefront," around whom the story’s central conflicts and personal struggles revolve.
  • A. Khan Arnabah
    Khan Arnabah is a town in southern Syria located in the Golan Heights region near the city of Quneitra.
  • B. George Khan
    George Khan is the strict, traditional Pakistani-British patriarch in the film "East Is East," whose attempts to impose his cultural values on his mixed-heritage family drive the story's central conflict.
  • C. Milini Khan
    Milini Khan is an American singer and the daughter of renowned R&B and funk vocalist Chaka Khan.
  • D. Khan Dauran
    Khan Dauran was a prominent Mughal military commander and nobleman of the early 18th century who played a key role in the empire’s defense during Nader Shah’s invasion of India.
  • E. Tauke Khan
    Tauke Khan was a prominent 17th–18th century Kazakh ruler known for unifying the Kazakh tribes and codifying traditional laws into the "Jety Jargy" legal code.
  • 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_69e245513a5c81908d5cb471b4fc429d completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f1797400fc8190bec26726f434f787 completed April 29, 2026, 3:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b8fd07e9481909ff8057024ea2d94 completed May 18, 2026, 10:16 p.m.
NEDg Description generation batch_6a0b92e5412c81909968a22571229283 completed May 18, 2026, 10:29 p.m.
NED2 Entity disambiguation (via description) batch_6a0b93668f0c8190b86094e42fa89c1d completed May 18, 2026, 10:32 p.m.
Created at: April 17, 2026, 3:23 p.m.