Triple
T32606369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saratoga (1937 film) |
E833531
|
entity |
| Predicate | leadCharacterPlayedByJeanHarlow |
P175011
|
FINISHED |
| Object | Carol Clayton |
—
|
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: Carol Clayton | Statement: [Saratoga (1937 film), leadCharacterPlayedByJeanHarlow, Carol Clayton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadCharacterPlayedByJeanHarlow Context triple: [Saratoga (1937 film), leadCharacterPlayedByJeanHarlow, Carol Clayton]
-
A.
leadCharacterPlayedByClarkGable
Indicates that the work’s lead character is portrayed by the actor Clark Gable.
-
B.
scenesCompletedByJeanHarlow
Indicates that the specified scenes are ones that were completed by Jean Harlow.
-
C.
barbaraStanwyckRole
Indicates that the subject is a role or character portrayed by Barbara Stanwyck.
-
D.
leadActressCharacterName
Indicates the name of the character portrayed by the lead actress in a given work.
-
E.
hasMarleneDietrichRoleType
Indicates that an entity has a specific type or category of role associated with Marlene Dietrich.
- 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_69f3492bfa648190b6ae472074634e29 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6cd9bae8c8190b528641499162a75 |
completed | May 3, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1470808190b70cdfd7a6395670 |
completed | May 3, 2026, 4:16 a.m. |
| PDg | Predicate description generation | batch_69f6cd119cac8190a0b3ebe8b9c742c2 |
completed | May 3, 2026, 4:20 a.m. |
Created at: May 1, 2026, 1:05 a.m.