Triple
T7918231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kronk's New Groove |
E183879
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Pamela Ziegenhagen-Shefland |
E684516
|
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: Pamela Ziegenhagen-Shefland | Statement: [Kronk's New Groove, editor, Pamela Ziegenhagen-Shefland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pamela Ziegenhagen-Shefland Context triple: [Kronk's New Groove, editor, Pamela Ziegenhagen-Shefland]
-
A.
Pamela Ziegenhagen-Shefland
chosen
Pamela Ziegenhagen-Shefland is a film editor known for her work on animated features, including Disney’s "Mickey, Donald, Goofy: The Three Musketeers."
-
B.
Pamela Pettler
Pamela Pettler is an American screenwriter best known for her work on darkly comedic animated films such as "Corpse Bride" and "Monster House."
-
C.
Pamela Martin
Pamela Martin is an American film editor known for her work on acclaimed movies such as "The Fighter" and "Little Miss Sunshine."
-
D.
Linda Gunderson
Linda Gunderson is a kind-hearted Minnesota bookshop owner who becomes the human protagonist and caretaker of the rare macaw Blu in the animated film "Rio."
-
E.
Janine Melnitz
Janine Melnitz is the Ghostbusters’ sharp-tongued, no-nonsense receptionist who provides comic relief and grounded support to the team.
- 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_69ca828efbe48190bd48482650182e79 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a8fbbb48190b50def4941761a31 |
completed | March 31, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbde69b608190a49d93c04c46787d |
completed | April 1, 2026, 6:40 a.m. |
Created at: March 30, 2026, 5:05 p.m.