Triple

T1678633
Position Surface form Disambiguated ID Type / Status
Subject Hugh Grant E36287 entity
Predicate participatedIn P149 FINISHED
Object Maurice
Maurice is a 1987 British romantic drama film, based on E.M. Forster’s novel, that explores same-sex love and class in early 20th-century England.
 E191283 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: Maurice | Statement: [Hugh Grant, participatedIn, Maurice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maurice
Context triple: [Hugh Grant, participatedIn, Maurice]
  • A. Maurice
    Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
  • B. Bernard
    Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
  • C. Georges
    Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
  • D. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • E. Jules
    Jules is a given name most famously associated with French poet Jules Laforgue, a key figure in Symbolist and early modernist literature.
  • 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: Maurice
Triple: [Hugh Grant, participatedIn, Maurice]
Generated description
Maurice is a 1987 British romantic drama film, based on E.M. Forster’s novel, that explores same-sex love and class in early 20th-century England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maurice
Target entity description: Maurice is a 1987 British romantic drama film, based on E.M. Forster’s novel, that explores same-sex love and class in early 20th-century England.
  • A. Maurice
    Maurice is a masculine given name of Latin origin, commonly used in English and French-speaking countries.
  • B. Bernard
    Bernard is a masculine given name of Old French and Germanic origin, historically borne by notable figures such as military leaders and saints.
  • C. Georges
    Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
  • D. Jacques
    Jacques is the French form of the given name James, commonly used in French-speaking countries.
  • E. Jules
    Jules is a given name most famously associated with French poet Jules Laforgue, a key figure in Symbolist and early modernist literature.
  • 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_69a886139ed081909af0940aa9313512 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa625f7e1081909c3c4fe76625783a completed March 6, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad798b0f1c81908174b312878e559b completed March 8, 2026, 1:28 p.m.
NEDg Description generation batch_69ad79ffb1f481908e91e87f131dd779 completed March 8, 2026, 1:30 p.m.
NED2 Entity disambiguation (via description) batch_69ad7b1a6c90819095b04638ff8a45d9 completed March 8, 2026, 1:35 p.m.
Created at: March 4, 2026, 7:29 p.m.