Triple
T26172676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the cotter's wife |
E654450
|
entity |
| Predicate | portrayedByAuthorAs |
P976
|
FINISHED |
| Object | model of Christian domestic womanhood |
—
|
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: model of Christian domestic womanhood | Statement: [the cotter's wife, portrayedByAuthorAs, model of Christian domestic womanhood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedByAuthorAs Context triple: [the cotter's wife, portrayedByAuthorAs, model of Christian domestic womanhood]
-
A.
describedByAuthorAs
chosen
Indicates that one entity is characterized, labeled, or portrayed in a particular way by an author.
-
B.
portrayedAsFrom
Indicates that one entity is depicted or represented as originating from, or belonging to, the place or source specified by another entity.
-
C.
portrayedByWork
Indicates that a work (such as a film, book, or artwork) depicts, represents, or portrays a particular entity.
-
D.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
E.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
- 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_69ee5b45873c81909499203612d05d07 |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69f6a28c7c148190bfc980aad9f678ca |
completed | May 3, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69f69fe1e3c88190830bb2e9f407357e |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 26, 2026, 8:36 p.m.