Triple
T36200718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Places |
E1047252
|
entity |
| Predicate | protagonistAgeAtPresentTimeline |
P37051
|
FINISHED |
| Object | early thirties |
—
|
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: early thirties | Statement: [Dark Places, protagonistAgeAtPresentTimeline, early thirties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistAgeAtPresentTimeline Context triple: [Dark Places, protagonistAgeAtPresentTimeline, early thirties]
-
A.
protagonistAge
chosen
Indicates the age of the main character or central figure in a narrative or scenario.
-
B.
protagonistAgeRelativeToPrequel
Indicates how the protagonist’s age in the current work compares to their age in a preceding prequel story.
-
C.
hasAgeingProtagonist
Indicates that the work features a main character who is experiencing aging or the later stages of life as a central aspect of their role or development.
-
D.
protagonistAgeDifferenceTheme
Indicates that the work thematically explores the significance or impact of age differences involving the protagonist.
-
E.
protagonistOriginTime
Indicates the point in time at which the protagonist first comes into existence or begins their story.
- 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_69f76e414bdc8190996f15a544220a3d |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7ba6d06f48190a71b5a2f19e2232f |
completed | May 3, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a4aad48190a62e41c5e39339d9 |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:08 p.m.