Triple
T20550412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zoe (film) |
E504580
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Emma |
—
|
NE NERFINISHED |
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: Emma | Statement: [Zoe (film), hasCharacter, Emma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Context triple: [Zoe (film), hasCharacter, Emma]
-
A.
Emma
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
-
B.
Emma
"Emma" is a 2009 British television miniseries adaptation of Jane Austen's novel, starring Romola Garai in the title role.
-
C.
Emma
Emma is a central character in Sam Shepard’s play "Curse of the Starving Class," portrayed as a rebellious and sharp-witted teenage girl struggling against her dysfunctional family and bleak circumstances.
-
D.
Emma
Emma is the central protagonist of Andrew Lloyd Webber's song cycle and musical "Tell Me on a Sunday," which follows a young Englishwoman navigating love and heartbreak in New York.
-
E.
Emma
chosen
Emma is a fictional character portrayed by American actress Ashley Scott, known for her roles in film and television.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b52c048190952b4d0f430813a3 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a5d739bc8190b2bf60f82d2ade0c |
completed | April 20, 2026, 10:16 p.m. |
Created at: April 16, 2026, 11:38 a.m.