Triple
T10325616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tully |
E242753
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Mackenzie Davis |
E226452
|
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: Mackenzie Davis | Statement: [Tully, starring, Mackenzie Davis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mackenzie Davis Context triple: [Tully, starring, Mackenzie Davis]
-
A.
Mackenzie Davis
chosen
Mackenzie Davis is a Canadian actress known for her roles in films like "Blade Runner 2049" and "The Martian" and the TV series "Halt and Catch Fire."
-
B.
MacKenzie Porter
MacKenzie Porter is a Canadian actress and country music singer known for her roles in television series and her successful recording career.
-
C.
Dichen Lachman
Dichen Lachman is a Nepalese-Australian actress and producer best known for her genre television roles in series such as Dollhouse, Agents of S.H.I.E.L.D., and The 100.
-
D.
Makena Lautner
Makena Lautner is the younger sister of American actor Taylor Lautner, known for maintaining a relatively private life outside of her brother’s fame.
-
E.
Suki Waterhouse
Suki Waterhouse is an English model, actress, and singer known for her fashion work, film roles, and music career.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7cd76348190b93562112300acfc |
completed | April 7, 2026, 10:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71da93b988190ad568b0677b5d344 |
completed | April 9, 2026, 3:31 a.m. |
Created at: April 6, 2026, 11:51 a.m.