Triple
T14302143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vivien Leigh |
E354592
|
entity |
| Predicate | portrayed |
P1668
|
FINISHED |
| Object | Scarlett O'Hara |
E48313
|
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: Scarlett O'Hara | Statement: [Vivien Leigh, portrayed, Scarlett O'Hara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scarlett O'Hara Context triple: [Vivien Leigh, portrayed, Scarlett O'Hara]
-
A.
Scarlett O'Hara
chosen
Scarlett O'Hara is the strong-willed, manipulative Southern belle who serves as the central heroine of Margaret Mitchell's Civil War–era novel "Gone with the Wind."
-
B.
Scarlett O'Connor
Scarlett O'Connor is a shy but talented singer-songwriter and one of the central characters in the television drama series "Nashville."
-
C.
Scarlett
Scarlett is the given name of American actress Scarlett Johansson, a prominent Hollywood star known for roles in films like "Lost in Translation" and the Marvel Cinematic Universe.
-
D.
Scarlett
Scarlett is a fictional burlesque performer character associated with The Burlesque Lounge setting.
-
E.
Scarlett
Scarlett is a sequel novel to Margaret Mitchell’s "Gone with the Wind," written by Alexandra Ripley and continuing the story of Scarlett O’Hara.
- 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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de717fc2348190bb6ba3109bd2871f |
completed | April 14, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d2883e081909c53170ef30b4125 |
completed | May 8, 2026, 1:32 a.m. |
Created at: April 10, 2026, 1:12 a.m.