Triple

T499220
Position Surface form Disambiguated ID Type / Status
Subject Shakespeare in Love E10362 entity
Predicate producer P490 FINISHED
Object Marc Norman
Marc Norman is an American screenwriter and producer best known for co-writing the Oscar-winning film "Shakespeare in Love."
 E62616 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: Marc Norman | Statement: [Shakespeare in Love, producer, Marc Norman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marc Norman
Context triple: [Shakespeare in Love, producer, Marc Norman]
  • A. Jonathan King
    Jonathan King is a British singer-songwriter, record producer, and former music industry figure known for his 1960s pop hits and later controversial legal troubles.
  • B. Peter Sargeant
    Peter Sargeant was a colonial-era jurist who served as a judge on the Court of Oyer and Terminer.
  • C. Alan Webber
    Alan Webber is an American politician and former business magazine co-founder who serves as the mayor of Santa Fe, New Mexico.
  • D. John Merrill
    John Merrill was an American architect best known as a co-founder of the influential international architecture and engineering firm Skidmore, Owings & Merrill.
  • E. Danny Cohen
    Danny Cohen is a British cinematographer known for his acclaimed work on films such as The King’s Speech and collaborations with director Tom Hooper.
  • 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: Marc Norman
Triple: [Shakespeare in Love, producer, Marc Norman]
Generated description
Marc Norman is an American screenwriter and producer best known for co-writing the Oscar-winning film "Shakespeare in Love."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marc Norman
Target entity description: Marc Norman is an American screenwriter and producer best known for co-writing the Oscar-winning film "Shakespeare in Love."
  • A. Jonathan King
    Jonathan King is a British singer-songwriter, record producer, and former music industry figure known for his 1960s pop hits and later controversial legal troubles.
  • B. Peter Sargeant
    Peter Sargeant was a colonial-era jurist who served as a judge on the Court of Oyer and Terminer.
  • C. Alan Webber
    Alan Webber is an American politician and former business magazine co-founder who serves as the mayor of Santa Fe, New Mexico.
  • D. John Merrill
    John Merrill was an American architect best known as a co-founder of the influential international architecture and engineering firm Skidmore, Owings & Merrill.
  • E. Danny Cohen
    Danny Cohen is a British cinematographer known for his acclaimed work on films such as The King’s Speech and collaborations with director Tom Hooper.
  • 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_69a2e847df8481909239ec08ccf1e376 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f119b14c8190a5a6b119579c2682 completed Feb. 28, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a48543f1948190b710628bf53cdf01 completed March 1, 2026, 6:28 p.m.
NEDg Description generation batch_69a48918e1d08190b2f51c0d0510ce15 completed March 1, 2026, 6:44 p.m.
NED2 Entity disambiguation (via description) batch_69a4899415108190b520b8e00fdf00ff completed March 1, 2026, 6:46 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.