Triple
T8446519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viviane |
E199689
|
entity |
| Predicate | relationshipToMerlin |
P38921
|
FINISHED |
| Object | student in magic (in some versions) |
—
|
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: student in magic (in some versions) | Statement: [Viviane, relationshipToMerlin, student in magic (in some versions)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMerlin Context triple: [Viviane, relationshipToMerlin, student in magic (in some versions)]
-
A.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
B.
relationshipToManfred
Indicates the specific type of relationship or connection that one entity has to the individual named Manfred.
-
C.
relationshipToHeadmaster
Indicates the specific type of personal or professional relationship an entity has with the headmaster.
-
D.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
E.
roleInHarryPotter
Indicates that one entity has a specific role or character part within the Harry Potter series in relation to the other 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe3152a3c819092efdeab718def7a |
completed | March 31, 2026, 3:07 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:09 p.m.