Triple

T2659151
Position Surface form Disambiguated ID Type / Status
Subject Teddy Daniels E54684 entity
Predicate occupation P3 FINISHED
Object U.S. Marshal
A U.S. Marshal is a federal law enforcement officer responsible for duties such as protecting the federal judiciary, transporting prisoners, and apprehending fugitives.
 E285566 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: U.S. Marshal | Statement: [Teddy Daniels, occupation, U.S. Marshal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: U.S. Marshal
Context triple: [Teddy Daniels, occupation, U.S. Marshal]
  • A. Sherif
    Sherif is a masculine given name of Arabic origin commonly used in Egypt and other Arabic-speaking countries.
  • B. Charles Starrett
    Charles Starrett was an American film actor best known for his long-running role as the Durango Kid in B-Western movies during the 1930s and 1940s.
  • C. The F.B.I.
    The F.B.I. is an American television crime drama series centered on the investigative work of agents from the Federal Bureau of Investigation.
  • D. Seriff
    Seriff is the surname of Marc Seriff, an American computer scientist and co-founder of America Online (AOL).
  • E. Nash Bridges
    Nash Bridges is an American television crime drama series set in San Francisco, starring Don Johnson as a charismatic police inspector.
  • 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: U.S. Marshal
Triple: [Teddy Daniels, occupation, U.S. Marshal]
Generated description
A U.S. Marshal is a federal law enforcement officer responsible for duties such as protecting the federal judiciary, transporting prisoners, and apprehending fugitives.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: U.S. Marshal
Target entity description: A U.S. Marshal is a federal law enforcement officer responsible for duties such as protecting the federal judiciary, transporting prisoners, and apprehending fugitives.
  • A. Sherif
    Sherif is a masculine given name of Arabic origin commonly used in Egypt and other Arabic-speaking countries.
  • B. Charles Starrett
    Charles Starrett was an American film actor best known for his long-running role as the Durango Kid in B-Western movies during the 1930s and 1940s.
  • C. The F.B.I.
    The F.B.I. is an American television crime drama series centered on the investigative work of agents from the Federal Bureau of Investigation.
  • D. Seriff
    Seriff is the surname of Marc Seriff, an American computer scientist and co-founder of America Online (AOL).
  • E. Nash Bridges
    Nash Bridges is an American television crime drama series set in San Francisco, starring Don Johnson as a charismatic police inspector.
  • 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_69abd94dcaa48190aec625f68ce61a02 completed March 7, 2026, 7:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98d765c48190a227137467b7dbe1 completed March 10, 2026, 4:06 a.m.
NEDg Description generation batch_69af9952a95881908d01aa13f5feef43 completed March 10, 2026, 4:08 a.m.
NED2 Entity disambiguation (via description) batch_69af99e68f10819094d758d3a4bc2e9c completed March 10, 2026, 4:11 a.m.
Created at: March 6, 2026, 9:53 p.m.