Triple
T37359072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cherubino |
E927530
|
entity |
| Predicate | relationshipToCountess |
P115415
|
FINISHED |
| Object | Countess’s page |
—
|
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: Countess’s page | Statement: [Cherubino, relationshipToCountess, Countess’s page]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToCountess Context triple: [Cherubino, relationshipToCountess, Countess’s page]
-
A.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
B.
relationshipToPrincess
Indicates the specific familial, social, or romantic connection that one entity has to a princess.
-
C.
relationshipToLady
chosen
Indicates the specific type of social, familial, or personal connection that one entity has to a lady.
-
D.
relationshipWithMiladyDeWinter
Indicates a personal or interpersonal connection that an entity has with Milady de Winter, such as familial, romantic, adversarial, or other significant relational ties.
-
E.
relationshipToEleanorVance
Indicates the specific nature or type of relationship an entity has with Eleanor Vance.
- 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_69f76eb701788190b40824bc4594d985 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a13a1308190a202df66f4781855 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:16 p.m.