Triple
T34706749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Persian Lessons |
E1000525
|
entity |
| Predicate | oscarSubmittedBy |
P181803
|
FINISHED |
| Object | Belarus |
—
|
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: Belarus | Statement: [Persian Lessons, oscarSubmittedBy, Belarus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oscarSubmittedBy Context triple: [Persian Lessons, oscarSubmittedBy, Belarus]
-
A.
oscarAward
Indicates that an entity has received or been honored with an Academy Award (Oscar).
-
B.
oscarRecord
Indicates that an entity has a record or entry associated with the Oscars, such as a nomination, win, or related recognition.
-
C.
oscarCategoryWon
Indicates that an entity has won an Academy Award in the specified Oscar category.
-
D.
bestStoryOscarCreditedTo
Indicates that an entity is credited as the recipient of the Academy Award (Oscar) for Best Original or Adapted Screenplay for a particular film or story.
-
E.
associatedAwardWinningFilm
Indicates that there is a relationship between an entity and a film with which it is connected, where that film has received an award.
- F. None of above. chosen
Provenance (4 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_69f76dab937881909c86f1b9ad50445f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f7824dc3f0819092a5102895b4a478 |
completed | May 3, 2026, 5:13 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
| PDg | Predicate description generation | batch_69f7817c79e081908e685c48165e086b |
completed | May 3, 2026, 5:10 p.m. |
Created at: May 3, 2026, 3:59 p.m.