Triple

T13695885
Position Surface form Disambiguated ID Type / Status
Subject Roc Marciano E328383 entity
Predicate associatedAct P37 FINISHED
Object Ka
Ka is an American underground rapper and producer from Brownsville, Brooklyn, known for his minimalist, introspective style and dense, poetic lyricism.
 E1054645 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: Ka | Statement: [Roc Marciano, associatedAct, Ka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ka
Context triple: [Roc Marciano, associatedAct, Ka]
  • A. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • B. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • C. KA
    KA is a postcode area in the United Kingdom covering parts of southwest Scotland, including towns such as Kilmarnock and Irvine.
  • D. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • E. KA
    KA is the IATA airline designator for Cathay Dragon, a former Hong Kong-based regional carrier owned by Cathay Pacific.
  • 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: Ka
Triple: [Roc Marciano, associatedAct, Ka]
Generated description
Ka is an American underground rapper and producer from Brownsville, Brooklyn, known for his minimalist, introspective style and dense, poetic lyricism.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ka
Target entity description: Ka is an American underground rapper and producer from Brownsville, Brooklyn, known for his minimalist, introspective style and dense, poetic lyricism.
  • A. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • B. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • C. KA
    KA is a postcode area in the United Kingdom covering parts of southwest Scotland, including towns such as Kilmarnock and Irvine.
  • D. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • E. KA
    KA is the IATA airline designator for Cathay Dragon, a former Hong Kong-based regional carrier owned by Cathay Pacific.
  • 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_69d8076ff62081908a7bd79889edd7a0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc8773f388190b2413b1e05fd5fd7 completed April 12, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69f794514afc8190b334b1fc74a6cdd5 completed May 3, 2026, 6:30 p.m.
NEDg Description generation batch_69f795e361c48190b37060312e7df181 completed May 3, 2026, 6:37 p.m.
NED2 Entity disambiguation (via description) batch_69f796e5c60c8190a19389bc4cdbd658 completed May 3, 2026, 6:41 p.m.
Created at: April 9, 2026, 9:54 p.m.