Triple

T2658004
Position Surface form Disambiguated ID Type / Status
Subject Romeo + Juliet E54660 entity
Predicate starring P1507 FINISHED
Object Dash Mihok
Dash Mihok is an American actor best known for his roles in films like "The Thin Red Line" and the TV series "Ray Donovan."
 E286393 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: Dash Mihok | Statement: [Romeo + Juliet, starring, Dash Mihok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dash Mihok
Context triple: [Romeo + Juliet, starring, Dash Mihok]
  • A. Mamoru
    Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
  • B. Hayato
    Hayato is a masculine Japanese given name commonly used for boys and borne by various notable figures in politics, sports, and entertainment.
  • C. Katsuya
    Katsuya is a Japanese given name commonly used for males.
  • D. Sanz
    Sanz is a prominent Hasidic dynasty known for its strong emphasis on Torah scholarship, strict halachic observance, and influential rabbinic leadership originating in 19th-century Galicia.
  • E. Miki
    Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
  • 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: Dash Mihok
Triple: [Romeo + Juliet, starring, Dash Mihok]
Generated description
Dash Mihok is an American actor best known for his roles in films like "The Thin Red Line" and the TV series "Ray Donovan."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dash Mihok
Target entity description: Dash Mihok is an American actor best known for his roles in films like "The Thin Red Line" and the TV series "Ray Donovan."
  • A. Mamoru
    Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
  • B. Hayato
    Hayato is a masculine Japanese given name commonly used for boys and borne by various notable figures in politics, sports, and entertainment.
  • C. Katsuya
    Katsuya is a Japanese given name commonly used for males.
  • D. Sanz
    Sanz is a prominent Hasidic dynasty known for its strong emphasis on Torah scholarship, strict halachic observance, and influential rabbinic leadership originating in 19th-century Galicia.
  • E. Miki
    Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
  • 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_69ab49e028948190b97e01d73548b1d9 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd94c61a08190bdf5e1caeff3e788 completed March 7, 2026, 7:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98d535388190979a549dc2ce5f2f completed March 10, 2026, 4:06 a.m.
NEDg Description generation batch_69af99c237548190838559ccac95f1c5 completed March 10, 2026, 4:10 a.m.
NED2 Entity disambiguation (via description) batch_69af9a5938b48190820f37f2e2280438 completed March 10, 2026, 4:13 a.m.
Created at: March 6, 2026, 9:53 p.m.