Triple
T23897310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Morgan |
E600938
|
entity |
| Predicate | playedVampireCharacter |
P36975
|
FINISHED |
| Object | Klaus Mikaelson |
—
|
NE NERFINISHED |
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: Klaus Mikaelson | Statement: [Joseph Morgan, playedVampireCharacter, Klaus Mikaelson]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playedVampireCharacter Context triple: [Joseph Morgan, playedVampireCharacter, Klaus Mikaelson]
-
A.
hasVampireCharacter
Indicates that an entity includes or features at least one character who is a vampire.
-
B.
hasPlayedRole
chosen
Indicates that an entity has performed or portrayed a particular role or character in some context (such as a film, play, or production).
-
C.
playedEarlyRoleIn
Indicates that one entity contributed significantly to the initial or formative stages of another entity’s development, success, or emergence.
-
D.
hasVampireMaker
Indicates that one entity is the creator or sire who turned another entity into a vampire.
-
E.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
- 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_69e295341ac0819080647f2908af793c |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1cdda39448190bdefa953e0558583 |
completed | April 29, 2026, 9:22 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:25 p.m.