Triple
T8742154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lady from Shanghai |
E207529
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Ted de Corsia |
E490154
|
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: Ted de Corsia | Statement: [The Lady from Shanghai, starring, Ted de Corsia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ted de Corsia Context triple: [The Lady from Shanghai, starring, Ted de Corsia]
-
A.
Ted de Corsia
chosen
Ted de Corsia was an American character actor known for his tough-guy roles in classic film noir and crime movies of the mid-20th century.
-
B.
Joe Corallo
Joe Corallo is a comic book writer and editor known for his work on independent and genre titles in the modern comics scene.
-
C.
Greg Corrado
Greg Corrado is an American computer scientist and researcher known for his pioneering work in artificial intelligence and deep learning, including co-founding Google Brain.
-
D.
Christopher Gattelli
Christopher Gattelli is a Tony Award–winning American choreographer known for his dynamic work on Broadway musicals, including the stage adaptation of Disney’s "Newsies."
-
E.
Anthony Marinelli
Anthony Marinelli is an American composer and musician best known for his film scores and extensive work in Hollywood soundtracks.
- 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d6fd5dc8190906b7147f27c5d46 |
completed | March 31, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf42f282e48190ad158063e265e0f0 |
completed | April 3, 2026, 4:32 a.m. |
Created at: March 30, 2026, 6:38 p.m.