Triple
T7494545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beauty and the Beast (2017 film) |
E177090
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Emma Watson |
E117460
|
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: Emma Watson | Statement: [Beauty and the Beast (2017 film), stars, Emma Watson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Watson Context triple: [Beauty and the Beast (2017 film), stars, Emma Watson]
-
A.
Emma Watson
chosen
Emma Watson is a British actress and activist best known for playing Hermione Granger in the Harry Potter film series and for her advocacy on gender equality.
-
B.
Evanna Lynch
Evanna Lynch is an Irish actress best known for playing Luna Lovegood in the Harry Potter film series.
-
C.
Diana Patricia Hiddleston
Diana Patricia Hiddleston is the mother of English actor Tom Hiddleston.
-
D.
Lily James
Lily James is an English actress known for her roles in films such as Cinderella, Baby Driver, and Mamma Mia! Here We Go Again, as well as the TV series Downton Abbey.
-
E.
Emma Hiddleston
Emma Hiddleston is a British actress and the younger sister of actor Tom Hiddleston.
- 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_69c69f2583808190bd1a4936c42a5815 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f57b5b4c8190ab839e6a98ee86ed |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c819f00819087fef27e4f4fdc1c |
completed | March 28, 2026, 8:39 p.m. |
Created at: March 27, 2026, 3:43 p.m.