Triple
T2547823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peggy Carter |
E57945
|
entity |
| Predicate | relativeTypeToSharonCarter |
P37304
|
FINISHED |
| Object | aunt or great-aunt (varies by continuity) |
—
|
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: aunt or great-aunt (varies by continuity) | Statement: [Peggy Carter, relativeTypeToSharonCarter, aunt or great-aunt (varies by continuity)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeTypeToSharonCarter Context triple: [Peggy Carter, relativeTypeToSharonCarter, aunt or great-aunt (varies by continuity)]
-
A.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
B.
relatedCharacter
chosen
Indicates that one character has a specified relationship or association with another character.
-
C.
relationshipToDonnaSheridan
Indicates the specific type of personal or familial connection an entity has with Donna Sheridan.
-
D.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
E.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
- 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_69ab4a5212d88190b989ce129f2ad87f |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd2e672948190bb7fe9b47535a172 |
completed | March 7, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69abd0c63964819092d5f578195ae8dd |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:47 p.m.