Triple
T11747643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rendition |
E279324
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Megan Gill |
E463164
|
NE FINISHED |
How this triple was built (2 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: [Rendition, editedBy, Megan Gill]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Gill Context triple: [Rendition, editedBy, Megan Gill]
-
A.
Megan Gill
chosen
Megan Gill is a film editor best known for her work on major feature films, including the superhero movie "X-Men Origins: Wolverine."
-
B.
Megan Holley
Megan Holley is an American screenwriter best known for writing the indie dramedy film "Sunshine Cleaning."
-
C.
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."
-
D.
Megan Ferguson
Megan Ferguson is an American actress known for her work in television comedies and dramas, including a prominent role in the series "The Comedians."
-
E.
Megan Morgan
Megan Morgan is a character from the 1988 sci-fi horror comedy film "Critters 2: The Main Course."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a50763a081908597da118bd0a64e |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d756bd08190a79adc9a2e6188ed |
completed | May 3, 2026, 2:36 p.m. |
Created at: April 8, 2026, 9:41 p.m.