Triple

T2351188
Position Surface form Disambiguated ID Type / Status
Subject Lady Macbeth E47451 entity
Predicate productionCompany P490 FINISHED
Object iFeatures
iFeatures is a UK-based low-budget film initiative and production scheme that supports emerging filmmakers in developing and producing feature films.
 E257881 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: iFeatures | Statement: [Lady Macbeth, productionCompany, iFeatures]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: iFeatures
Context triple: [Lady Macbeth, productionCompany, iFeatures]
  • A. IES
    IES is the research, evaluation, and statistics arm of the U.S. Department of Education that provides rigorous evidence to inform education policy and practice.
  • B. ICA
    ICA is a contemporary art museum in Philadelphia known for its innovative exhibitions and support of emerging artists.
  • C. ips
    ips is an IETF working group focused on developing standards for transporting storage protocols over IP networks.
  • D. iFCP
    iFCP is an Internet protocol that enables Fibre Channel storage traffic to be transported over IP networks, facilitating long-distance and wide-area storage networking.
  • E. IC
    IC is a common abbreviation for Iowa City, a university-centered community best known as the home of the University of Iowa and its renowned Writers' Workshop.
  • 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: iFeatures
Triple: [Lady Macbeth, productionCompany, iFeatures]
Generated description
iFeatures is a UK-based low-budget film initiative and production scheme that supports emerging filmmakers in developing and producing feature films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: iFeatures
Target entity description: iFeatures is a UK-based low-budget film initiative and production scheme that supports emerging filmmakers in developing and producing feature films.
  • A. IES
    IES is the research, evaluation, and statistics arm of the U.S. Department of Education that provides rigorous evidence to inform education policy and practice.
  • B. ICA
    ICA is a contemporary art museum in Philadelphia known for its innovative exhibitions and support of emerging artists.
  • C. ips
    ips is an IETF working group focused on developing standards for transporting storage protocols over IP networks.
  • D. iFCP
    iFCP is an Internet protocol that enables Fibre Channel storage traffic to be transported over IP networks, facilitating long-distance and wide-area storage networking.
  • E. IC
    IC is a common abbreviation for Iowa City, a university-centered community best known as the home of the University of Iowa and its renowned Writers' Workshop.
  • 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_69a88a1b678c8190bce986922ba60ce0 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc6f75d888190a2e41edaa532e83f completed March 7, 2026, 6:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae962f769881909a7713880eaa9b84 completed March 9, 2026, 9:43 a.m.
NEDg Description generation batch_69ae96b399608190bfa846c433612142 completed March 9, 2026, 9:45 a.m.
NED2 Entity disambiguation (via description) batch_69ae977e539c81909cef638cc61e5ec1 completed March 9, 2026, 9:48 a.m.
Created at: March 4, 2026, 7:54 p.m.