Triple
T25022195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I'll Be Seeing You |
E626605
|
entity |
| Predicate | maleLeadCharacterOccupation |
P21567
|
FINISHED |
| Object | soldier |
—
|
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: soldier | Statement: [I'll Be Seeing You, maleLeadCharacterOccupation, soldier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maleLeadCharacterOccupation Context triple: [I'll Be Seeing You, maleLeadCharacterOccupation, soldier]
-
A.
originalMaleLeadRole
Indicates that an entity was the first male actor to play a particular leading role in a work or production.
-
B.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
C.
maleLeadActor
Indicates that the subject is the primary male actor in a performance or production.
-
D.
originalLeadActorRole
Indicates the role originally played by a particular lead actor in a given production or work.
-
E.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
- 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_69e2ff28ee3881909c626af002457a4a |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f44baa98588190a51b95d4a72313b7 |
completed | May 1, 2026, 6:43 a.m. |
| PD | Predicate disambiguation | batch_69f442c0c2e88190acd7f170f10ccef6 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 6:07 a.m.