Triple
T8033072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Logan |
E187033
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Lauren Shuler Donner |
E166986
|
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: Lauren Shuler Donner | Statement: [Logan, producer, Lauren Shuler Donner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Shuler Donner Context triple: [Logan, producer, Lauren Shuler Donner]
-
A.
Lauren Shuler Donner
chosen
Lauren Shuler Donner is an American film producer best known for her work on major studio films including the X-Men franchise and other popular Hollywood features.
-
B.
Natalie Desselle
Natalie Desselle was an American actress best known for her comedic roles in film and television, including her memorable performance in the 1997 adaptation of "Cinderella."
-
C.
Kirsten Nelson
Kirsten Nelson is an American actress best known for her role as police chief Karen Vick on the television series "Psych."
-
D.
Heather Matarazzo
Heather Matarazzo is an American actress best known for her character roles in films like "Welcome to the Dollhouse" and "The Princess Diaries."
-
E.
Ari Wegner
Ari Wegner is an acclaimed Australian cinematographer known for her visually striking work on films such as "The Power of the Dog."
- 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_69ca82ae2d1081909dbfee42b41db419 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3ef3e6848190913c4b1bef506aae |
completed | March 31, 2026, 3:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbe4211bc8190a784c19f11f17a39 |
completed | April 1, 2026, 6:42 a.m. |
Created at: March 30, 2026, 5:22 p.m.