Triple
T22765931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susanna Ingersoll |
E563124
|
entity |
| Predicate | relationshipToNathanielHawthorne |
P149641
|
FINISHED |
| Object | cousin |
—
|
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: cousin | Statement: [Susanna Ingersoll, relationshipToNathanielHawthorne, cousin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToNathanielHawthorne Context triple: [Susanna Ingersoll, relationshipToNathanielHawthorne, cousin]
-
A.
relationshipToHesterPrynne
Indicates the specific familial, social, or emotional connection that an entity has to Hester Prynne.
-
B.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
C.
relationshipToIsabelArcher
Indicates the specific personal or social connection that an entity has to Isabel Archer.
-
D.
relationshipWithIsabelArcher
Indicates that there exists a specific kind of interpersonal relationship or connection between an entity and Isabel Archer.
-
E.
relationshipToRogerChillingworth
Indicates the specific interpersonal connection or role that one entity has in relation to Roger Chillingworth.
- 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_69e24552e11c81909c2d61578a558bd7 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17a80249c819091569e7b8d500b45 |
completed | April 29, 2026, 3:26 a.m. |
| PD | Predicate disambiguation | batch_69eed2b88d88819096015deb6a648801 |
completed | April 27, 2026, 3:06 a.m. |
| PDg | Predicate description generation | batch_69eeeb5681f88190821129ced752f190 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:26 p.m.