Triple
T5514793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Glenn Miller Story |
E144654
|
entity |
| Predicate | leadActressPlays |
P6108
|
FINISHED |
| Object | June Allyson as Helen Burger Miller |
—
|
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: June Allyson as Helen Burger Miller | Statement: [The Glenn Miller Story, leadActressPlays, June Allyson as Helen Burger Miller]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActressPlays Context triple: [The Glenn Miller Story, leadActressPlays, June Allyson as Helen Burger Miller]
-
A.
leadActress
chosen
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
playedBy
Indicates that a role, character, or performance is portrayed or executed by a specific person or agent.
-
C.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
-
D.
leadActorNominee
Indicates that an entity was nominated for a lead acting role in relation to a particular work or award.
-
E.
actingRoleType
Indicates the specific type or category of role an entity performs when acting in a particular capacity or function.
- 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_69c008f77ff88190b0cd50ca207295d1 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f5b4e988190b590b4157cf089c1 |
completed | March 22, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69c01b0a06348190b39ac9fe80d2836a |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:33 p.m.