Triple

T5142413
Position Surface form Disambiguated ID Type / Status
Subject A24 E115987 entity
Predicate notableWork P4 FINISHED
Object Beef
Beef is a dark comedy-drama television series that follows an escalating feud between two strangers after a road rage incident, produced and distributed by the independent entertainment company A24.
 E498012 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: Beef | Statement: [A24, notableWork, Beef]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Beef
Context triple: [A24, notableWork, Beef]
  • A. Kobe beef
    Kobe beef is a highly prized, richly marbled wagyu beef from Japan renowned for its exceptional tenderness and flavor.
  • B. Hammon
    Hammon is the surname of Becky Hammon, a prominent basketball coach and former professional player.
  • C. Carnes
    Carnes is a surname of English origin borne by various individuals and fictional characters, including Ado Annie Carnes from the musical "Oklahoma!".
  • D. Lamb
    Lamb is a common English surname of Anglo-Saxon origin, often derived from a nickname or occupational name.
  • E. Bacon
    Bacon is a common English surname historically associated with notable figures such as the philosopher and statesman Francis Bacon.
  • 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: Beef
Triple: [A24, notableWork, Beef]
Generated description
Beef is a dark comedy-drama television series that follows an escalating feud between two strangers after a road rage incident, produced and distributed by the independent entertainment company A24.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Beef
Target entity description: Beef is a dark comedy-drama television series that follows an escalating feud between two strangers after a road rage incident, produced and distributed by the independent entertainment company A24.
  • A. Kobe beef
    Kobe beef is a highly prized, richly marbled wagyu beef from Japan renowned for its exceptional tenderness and flavor.
  • B. Hammon
    Hammon is the surname of Becky Hammon, a prominent basketball coach and former professional player.
  • C. Carnes
    Carnes is a surname of English origin borne by various individuals and fictional characters, including Ado Annie Carnes from the musical "Oklahoma!".
  • D. Lamb
    Lamb is a common English surname of Anglo-Saxon origin, often derived from a nickname or occupational name.
  • E. Bacon
    Bacon is a common English surname historically associated with notable figures such as the philosopher and statesman Francis Bacon.
  • 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_69bd4446c0e08190a7c29dc74976bf03 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd787ff1c081909a6954aa76e12cbf completed March 20, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69becfe7370c8190a47070487b461114 completed March 21, 2026, 5:05 p.m.
NEDg Description generation batch_69bed07c9cd081908f8246e24b2f6458 completed March 21, 2026, 5:08 p.m.
NED2 Entity disambiguation (via description) batch_69bed1013fe88190b76855a042226359 completed March 21, 2026, 5:10 p.m.
Created at: March 20, 2026, 1:43 p.m.