Triple
T20524216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep in My Heart |
E503891
|
entity |
| Predicate | emmyCategoryWon |
P140419
|
FINISHED |
| Object | Outstanding Lead Actress in a Miniseries or a Movie |
—
|
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: Outstanding Lead Actress in a Miniseries or a Movie | Statement: [Deep in My Heart, emmyCategoryWon, Outstanding Lead Actress in a Miniseries or a Movie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emmyCategoryWon Context triple: [Deep in My Heart, emmyCategoryWon, Outstanding Lead Actress in a Miniseries or a Movie]
-
A.
emmyAwardFor
Indicates that an entity has received or is associated with a specific Emmy Award for a particular work or achievement.
-
B.
hasEmmyWinningRole
Indicates that an entity has performed a role for which they (or the role) received an Emmy Award.
-
C.
oscarCategoryWon
Indicates that an entity has won an Academy Award in the specified Oscar category.
-
D.
emmyAwardsCount
Indicates the number of Emmy Awards that an entity has received.
-
E.
emmyAwardYear
Indicates the year in which an entity received or was associated with an Emmy 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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f48691c8190af0ac959e92e10d9 |
completed | April 20, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69e59fdb7ad88190924176c32a195db3 |
completed | April 20, 2026, 3:39 a.m. |
| PDg | Predicate description generation | batch_69e5a6a824748190bbe6192d73f3c613 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 16, 2026, 11:37 a.m.