Triple
T6161859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Princess Diaries |
E137460
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Bruce Green |
E286424
|
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: Bruce Green | Statement: [The Princess Diaries, editedBy, Bruce Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bruce Green Context triple: [The Princess Diaries, editedBy, Bruce Green]
-
A.
Bruce Green
chosen
Bruce Green is a film editor known for his work on feature films including the 1995 drama "The Basketball Diaries."
-
B.
Scott Green
Scott Green is a former National Football League official best known for serving as a referee in multiple Super Bowls.
-
C.
Scott Green
Scott Green is an American higher-education administrator and business executive who serves as president of the University of Idaho.
-
D.
Benjamin Green
Benjamin Green was a 19th-century British architect best known for designing prominent public monuments and buildings in northern England.
-
E.
Mark Greene
Mark Greene is a central fictional emergency physician and one of the original main characters on the television series "ER."
- 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_69c008a54fc88190b6ce4416490ca79d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d371484819090c18b62b095b49e |
completed | March 22, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14199f024819089af02b1c0eebfad |
completed | March 23, 2026, 1:35 p.m. |
Created at: March 22, 2026, 4:17 p.m.