Triple
T28491895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel, Rachel |
E720993
|
entity |
| Predicate | goldenGlobeAwardWin |
P47811
|
FINISHED |
| Object | Best Motion Picture – Drama |
—
|
LITERAL 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: Best Motion Picture – Drama | Statement: [Rachel, Rachel, goldenGlobeAwardWin, Best Motion Picture – Drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goldenGlobeAwardWin Context triple: [Rachel, Rachel, goldenGlobeAwardWin, Best Motion Picture – Drama]
-
A.
goldenGlobeAwardFor
Indicates that an entity has received a Golden Globe Award for a specified work, role, or achievement.
-
B.
goldenGlobeNomination
Indicates that an entity received a nomination for a Golden Globe award.
-
C.
hasWonGoldenGlobe
chosen
Indicates that the subject has received at least one Golden Globe award.
-
D.
goldenGlobesCeremony
Indicates that an entity is a specific Golden Globe Awards ceremony event, typically associated with a particular year or edition.
-
E.
oscarCategoryWon
Indicates that an entity has won an Academy Award in the specified Oscar category.
- F. None of above.
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_69f01a5a47148190b0a7e111bc432e0a |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f64f15a788819088d0175e20b8267b |
completed | May 2, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 3:01 a.m.