Triple
T20674572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joi |
E508125
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object | Denis Villeneuve |
—
|
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: Denis Villeneuve | Statement: [Joi, createdBy, Denis Villeneuve]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Denis Villeneuve Context triple: [Joi, createdBy, Denis Villeneuve]
-
A.
Denis Villeneuve
chosen
Denis Villeneuve is a critically acclaimed Canadian film director known for visually striking, atmospheric works such as Arrival, Blade Runner 2049, and the Dune films.
-
B.
Richard Comeau
Richard Comeau is a Canadian film editor known for his work on numerous acclaimed feature films, including the drama "Two Lovers and a Bear."
-
C.
Chris Noonan
Chris Noonan is an Australian film director best known for helming the acclaimed family film "Babe" and later the biographical drama "Miss Potter."
-
D.
Joseph Kosinski
Joseph Kosinski is an American film director known for visually striking, effects-driven blockbusters such as Tron: Legacy, Oblivion, and Top Gun: Maverick.
-
E.
Martin Arjovsky
Martin Arjovsky is a machine learning researcher best known for introducing the Wasserstein GAN, a generative adversarial network variant that improves training stability and sample quality.
- 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_69e0b4c1164881909a3bf1e3ddb2bc32 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5cdbec48190a945261c41d810ce |
completed | April 20, 2026, 11:25 p.m. |
Created at: April 16, 2026, 11:44 a.m.