Triple

T8155745
Position Surface form Disambiguated ID Type / Status
Subject Two Weeks Notice E190445 entity
Predicate producer P490 FINISHED
Object Mary McLaglen
Mary McLaglen is a film producer known for her work on major Hollywood movies, including romantic comedies and action films.
 E719843 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: Mary McLaglen | Statement: [Two Weeks Notice, producer, Mary McLaglen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mary McLaglen
Context triple: [Two Weeks Notice, producer, Mary McLaglen]
  • A. Eileen Ryan
    Eileen Ryan was an American actress known for her character roles in film and television and as the mother of actor and director Sean Penn.
  • B. Virginia O'Brien
    Virginia O'Brien was an American film actress and singer best known for her deadpan comedic style and musical performances in MGM musicals of the 1940s.
  • C. Mary Durkan
    Mary Durkan is an Irish politician known for her involvement in local and national public affairs.
  • D. Helen O’Connell
    Helen O’Connell was a popular American big band singer and entertainer best known for her work with Jimmy Dorsey’s orchestra in the 1940s.
  • E. Karen O’Brien
    Karen O’Brien is a British academic and university leader who serves as Vice-Chancellor of Durham University, overseeing its strategic direction and academic mission.
  • 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: Mary McLaglen
Triple: [Two Weeks Notice, producer, Mary McLaglen]
Generated description
Mary McLaglen is a film producer known for her work on major Hollywood movies, including romantic comedies and action films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mary McLaglen
Target entity description: Mary McLaglen is a film producer known for her work on major Hollywood movies, including romantic comedies and action films.
  • A. Eileen Ryan
    Eileen Ryan was an American actress known for her character roles in film and television and as the mother of actor and director Sean Penn.
  • B. Virginia O'Brien
    Virginia O'Brien was an American film actress and singer best known for her deadpan comedic style and musical performances in MGM musicals of the 1940s.
  • C. Mary Durkan
    Mary Durkan is an Irish politician known for her involvement in local and national public affairs.
  • D. Helen O’Connell
    Helen O’Connell was a popular American big band singer and entertainer best known for her work with Jimmy Dorsey’s orchestra in the 1940s.
  • E. Karen O’Brien
    Karen O’Brien is a British academic and university leader who serves as Vice-Chancellor of Durham University, overseeing its strategic direction and academic mission.
  • 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_69ca82bfeb6481909d07b91b5cf69f59 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb44d725b88190b77dc7537c1fa95d completed March 31, 2026, 3:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69cced43a5448190b2600cbdbabf9d31 completed April 1, 2026, 10:02 a.m.
NEDg Description generation batch_69ccf1b5f95481909fdb08d00d06023e completed April 1, 2026, 10:21 a.m.
NED2 Entity disambiguation (via description) batch_69cd055292088190927046793cec1c36 completed April 1, 2026, 11:45 a.m.
Created at: March 30, 2026, 5:37 p.m.