Triple
T5044851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the Convent |
E113636
|
entity |
| Predicate | roleInOpeningOfNovel |
P15535
|
FINISHED |
| Object | site of the initial violent scene |
—
|
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: site of the initial violent scene | Statement: [the Convent, roleInOpeningOfNovel, site of the initial violent scene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInOpeningOfNovel Context triple: [the Convent, roleInOpeningOfNovel, site of the initial violent scene]
-
A.
roleInStories
Indicates the specific function, position, or character part an entity plays within one or more stories.
-
B.
literaryRole
chosen
Indicates the specific narrative or functional role an entity holds within a literary work or text.
-
C.
narratorRole
Indicates that one entity serves as the narrator of another entity (such as a story, text, or media work), specifying the narrative role or function it performs.
-
D.
roleInScene
Indicates that an entity participates in a particular scene with a specific role or function within that scene.
-
E.
characterStatusInStory
Indicates the role or condition a character holds within the context of a specific story or narrative.
- 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_69bd44391fc48190a311ce9c826c209b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73fd81788190b7799f519277119a |
completed | March 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:37 p.m.