Triple
T26249634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Weston |
E656544
|
entity |
| Predicate | roleInEmma |
P140406
|
FINISHED |
| Object | Maternal figure to Emma Woodhouse |
—
|
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: Maternal figure to Emma Woodhouse | Statement: [Mrs. Weston, roleInEmma, Maternal figure to Emma Woodhouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInEmma Context triple: [Mrs. Weston, roleInEmma, Maternal figure to Emma Woodhouse]
-
A.
roleInAmy
Indicates that an entity has a specific role or function within the context of Amy (e.g., in Amy’s life, work, or activities).
-
B.
roleOfCharacter
chosen
Indicates that one entity serves as the narrative or functional role played by a character within a story, scenario, or context.
-
C.
roleInDialogue
Indicates that an entity participates in a dialogue with a specific conversational role (e.g., speaker, listener, moderator) relative to other participants.
-
D.
roleInName
Indicates that a specific role, title, or position is included as part of an entity’s name or naming expression.
-
E.
roleInFrancesHa
Indicates that one entity plays a specific role or character in the film "Frances Ha" in relation to another entity.
- 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_69ee5b4d25ac819086acb51184602576 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
Created at: April 26, 2026, 9:06 p.m.