Triple
T13550362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silas Selleck |
E323627
|
entity |
| Predicate | associatedWithCharacter |
P1481
|
FINISHED |
| Object | Rose Ross |
E323628
|
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: Rose Ross | Statement: [Silas Selleck, associatedWithCharacter, Rose Ross]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose Ross Context triple: [Silas Selleck, associatedWithCharacter, Rose Ross]
-
A.
Rose Ross
chosen
Rose Ross is a central character in the 2015 Western film "Slow West," around whom much of the story’s journey and conflict revolve.
-
B.
Sarah Ross
Sarah Ross is a retired CIA analyst drawn back into the world of espionage and action in the comedy-action film "Red 2."
-
C.
Charlotte Ross
Charlotte Ross is an American actress best known for her television roles on series such as NYPD Blue, Days of Our Lives, and Glee.
-
D.
Rose Lam
Rose Lam is a television producer best known for her executive production work on the Netflix adaptation of "A Series of Unfortunate Events."
-
E.
Tessa Ross
Tessa Ross is a prominent British film and television producer known for backing acclaimed, often auteur-driven projects across UK cinema and high-end TV drama.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaff0a6548190b8cde5084cef0061 |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75da4e19c819090d649b60a2dd410 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:46 p.m.