Triple
T14008874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ghostbusters (2016 film) |
E337024
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Katie Dippold |
E1056807
|
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: Katie Dippold | Statement: [Ghostbusters (2016 film), screenwriter, Katie Dippold]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katie Dippold Context triple: [Ghostbusters (2016 film), screenwriter, Katie Dippold]
-
A.
Katie Dippold
chosen
Katie Dippold is an American screenwriter and producer best known for her work on the comedy film "The Heat" and for writing on shows like "Parks and Recreation."
-
B.
Katie Luber
Katie Luber is an American art museum director and curator known for leading major institutions, including the Minneapolis Institute of Art.
-
C.
Katie Yeager
Katie Yeager is a reality television personality best known for appearing as one of the young mothers on MTV’s Teen Mom 3.
-
D.
Kaitlyn Dunn
Kaitlyn Dunn is a person notable enough to be specifically referenced as a bearer of the surname Dunn.
-
E.
Katie Carr
Katie Carr is the conflicted, self-critical doctor and wife who narrates Nick Hornby’s novel "How to Be Good," exploring themes of morality, marriage, and modern middle-class guilt.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed44f90819099ad08c09c066b56 |
completed | April 14, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd192ee1ac81908663ebd2e06784f1 |
completed | May 7, 2026, 10:58 p.m. |
Created at: April 9, 2026, 10:19 p.m.