Triple
T19695079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | There Will Come Soft Rains |
E472934
|
entity |
| Predicate | hasNoHumanProtagonist |
P136952
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [There Will Come Soft Rains, hasNoHumanProtagonist, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoHumanProtagonist Context triple: [There Will Come Soft Rains, hasNoHumanProtagonist, true]
-
A.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
B.
hasChildProtagonist
Indicates that the work features a child as its main or central character.
-
C.
hasSpiritProtagonist
Indicates that the primary or central character in a narrative is a spirit or non-corporeal being.
-
D.
doesNotFeatureCharacterDirectly
Indicates that the subject work does not include the specified character as an on-screen, on-page, or otherwise directly appearing participant in its content.
-
E.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6421385e88190b22b12ab3d851dea |
completed | April 20, 2026, 3:11 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
| PDg | Predicate description generation | batch_69e532bbedf081908d801600e2af94a7 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 1:46 p.m.