Triple
T28923931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Claythorne |
E733592
|
entity |
| Predicate | relationshipToCyrilHamilton |
P202616
|
FINISHED |
| Object | governess |
—
|
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: governess | Statement: [Vera Claythorne, relationshipToCyrilHamilton, governess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToCyrilHamilton Context triple: [Vera Claythorne, relationshipToCyrilHamilton, governess]
-
A.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
-
B.
relationshipTypeWithCeliaCoplestone
Indicates the specific nature or category of relationship that an entity has with Celia Coplestone.
-
C.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
D.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
E.
relationToStephenI
Indicates a familial or social relationship that an entity has specifically with Stephen I.
- 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_69f05b0a5cc0819094828367ae204b70 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_6a00a15604fc8190b1c280794960f3a4 |
completed | May 10, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_6a00a0f7e77881909ac85755ab0e6329 |
completed | May 10, 2026, 3:15 p.m. |
| PDg | Predicate description generation | batch_6a00a154c3b0819088de3f22f3f81040 |
completed | May 10, 2026, 3:16 p.m. |
Created at: April 28, 2026, 8:22 a.m.