Triple
T3633436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | X-Men Origins: Wolverine |
E77010
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Megan Gill
Megan Gill is a film editor best known for her work on major feature films, including the superhero movie "X-Men Origins: Wolverine."
|
E463164
|
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: Megan Gill | Statement: [X-Men Origins: Wolverine, editedBy, Megan Gill]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Gill Context triple: [X-Men Origins: Wolverine, editedBy, Megan Gill]
-
A.
Megan Holley
Megan Holley is an American screenwriter best known for writing the indie dramedy film "Sunshine Cleaning."
-
B.
Megan Hipwell
Megan Hipwell is a troubled young woman whose mysterious disappearance drives the central suspense and emotional tension in the psychological thriller film "The Girl on the Train."
-
C.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
D.
Megan McArthur
Megan McArthur is a NASA astronaut and oceanographer best known for her role as a mission specialist on Space Shuttle missions, including the final Hubble Space Telescope servicing flight.
-
E.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
- 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: Megan Gill Triple: [X-Men Origins: Wolverine, editedBy, Megan Gill]
Generated description
Megan Gill is a film editor best known for her work on major feature films, including the superhero movie "X-Men Origins: Wolverine."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Megan Gill Target entity description: Megan Gill is a film editor best known for her work on major feature films, including the superhero movie "X-Men Origins: Wolverine."
-
A.
Megan Holley
Megan Holley is an American screenwriter best known for writing the indie dramedy film "Sunshine Cleaning."
-
B.
Megan Hipwell
Megan Hipwell is a troubled young woman whose mysterious disappearance drives the central suspense and emotional tension in the psychological thriller film "The Girl on the Train."
-
C.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
D.
Megan McArthur
Megan McArthur is a NASA astronaut and oceanographer best known for her role as a mission specialist on Space Shuttle missions, including the final Hubble Space Telescope servicing flight.
-
E.
Megan Foster
Megan Foster is an American local government leader serving as the mayor of Coralville, Iowa.
- 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_69ad85dd0be48190b738990cb20c4731 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc30457608190840fb5b33f9965c4 |
completed | March 8, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be10079a408190ae09d3df55ead73c |
completed | March 21, 2026, 3:27 a.m. |
| NEDg | Description generation | batch_69be10cce8d0819093c9f1c721142e48 |
completed | March 21, 2026, 3:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be119987bc8190b0b0f75a2c07a60c |
completed | March 21, 2026, 3:33 a.m. |
Created at: March 8, 2026, 3:23 p.m.