Triple
T34965718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | After Dark |
E1008389
|
entity |
| Predicate | frameStorySpouseRole |
P30304
|
FINISHED |
| Object | recorder of his tales |
—
|
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: recorder of his tales | Statement: [After Dark, frameStorySpouseRole, recorder of his tales]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frameStorySpouseRole Context triple: [After Dark, frameStorySpouseRole, recorder of his tales]
-
A.
spouseOfRole
Indicates that one role is the spouse (husband, wife, or equivalent marital partner) of another role.
-
B.
hasSpouseInStory
chosen
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
C.
spouseCharacterOf
Indicates a marital relationship where one character is the spouse of another character.
-
D.
spouseRoleInHistory
Indicates that one entity serves or is recognized as the spouse of another entity within a specific historical context or period.
-
E.
spouseCharacterization
Indicates how one spouse describes, evaluates, or characterizes the other spouse within their relationship.
- 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_69f76dc69564819099e9e78aed6ff0a6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4 p.m.