Triple
T25022197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I'll Be Seeing You |
E626605
|
entity |
| Predicate | femaleLeadCharacterStatus |
P157549
|
FINISHED |
| Object | prisoner on furlough |
—
|
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: prisoner on furlough | Statement: [I'll Be Seeing You, femaleLeadCharacterStatus, prisoner on furlough]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleLeadCharacterStatus Context triple: [I'll Be Seeing You, femaleLeadCharacterStatus, prisoner on furlough]
-
A.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
-
B.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
C.
numberOfMainFemaleLeadsInWork
Indicates the number of primary female lead characters that appear in a given work.
-
D.
relationshipTypeWithFemaleLead
Indicates the type or nature of a relationship that an entity has with a female lead.
-
E.
hasFemaleTitleCharacter
Indicates that the subject work includes at least one female character whose title or role is explicitly referenced in its title.
- 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_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. |
| PDg | Predicate description generation | batch_69f448fe11f08190bdd53ca7ba2d51e4 |
completed | May 1, 2026, 6:32 a.m. |
Created at: April 18, 2026, 6:07 a.m.