Triple
T12915388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Darrow |
E308967
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object | Ruth Rose |
E55210
|
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: Ruth Rose | Statement: [Ann Darrow, createdBy, Ruth Rose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruth Rose Context triple: [Ann Darrow, createdBy, Ruth Rose]
-
A.
Ruth Rose
chosen
Ruth Rose was an American screenwriter best known for co-writing the classic 1933 monster film "King Kong."
-
B.
Ruth Henshaw
Ruth Henshaw is a fictional character portrayed by American actress Frances Rafferty, likely in mid-20th-century film or television.
-
C.
Ruth Harper
Ruth Harper was the wife of influential American sociologist C. Wright Mills, known primarily in relation to his personal and intellectual biography.
-
D.
Ruth Taylor
Ruth Taylor was an American film actress of the silent and early sound era, best remembered for her comedic roles in the late 1920s.
-
E.
Ruth Ellsworth
Ruth Ellsworth is a composer known for creating the musical score for the game Crossfire.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a0d6508190bca9668e9e06abfe |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a1a97748190992fe28c6411c4de |
completed | May 3, 2026, 8:40 a.m. |
Created at: April 9, 2026, 5:41 p.m.