Triple
T18112804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Then She Found Me |
E433521
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Pam Wise |
—
|
NE NERFINISHED |
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: Pam Wise | Statement: [Then She Found Me, editedBy, Pam Wise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pam Wise Context triple: [Then She Found Me, editedBy, Pam Wise]
-
A.
Pam Wise
chosen
Pam Wise is a film editor best known for her work on the acclaimed independent drama "Transamerica."
-
B.
Pamela Jenkins
Pamela Jenkins is a fictional character from the Saw horror film franchise, appearing in the movie "Saw VI."
-
C.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
D.
Pamela Frank
Pamela Frank is the second wife of singer and civil rights activist Harry Belafonte, known primarily for her long-term marriage to the entertainer.
-
E.
Pam Marsden
Pam Marsden is a film producer best known for her work on animated features, including serving as a producer on Disney's "Dinosaur" (2000).
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd3fd9c81909bfe95927f7553e3 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 10:28 a.m.