Triple
T1989539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Löwenstein |
E43219
|
entity |
| Predicate | isAssociatedWithProfessionOfBearer |
P35215
|
FINISHED |
| Object | actor |
—
|
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: actor | Statement: [Löwenstein, isAssociatedWithProfessionOfBearer, actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAssociatedWithProfessionOfBearer Context triple: [Löwenstein, isAssociatedWithProfessionOfBearer, actor]
-
A.
isAssociatedWith
Indicates that there exists a connection, relationship, or involvement between two entities without specifying its exact nature.
-
B.
recognizesProfession
Indicates that one entity acknowledges or identifies another entity’s professional role or occupation as such.
-
C.
hasProfessionalStatus
Indicates that an entity holds a particular professional standing, rank, or qualification within a field or occupation.
-
D.
derivesFromOccupation
Indicates that one entity originates from, is obtained through, or is a result of another entity’s occupation or professional role.
-
E.
hasOrganizationalRole
Indicates that an entity holds a specific role, position, or function within an organization.
- 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_69a88714cf2c819081644be450b8356e |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8ee02dc81908fec9fd8df7a4f40 |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb79ad6888190be99943a9c73cf3e |
completed | March 7, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69abb8ec608c81908917e945e0118ac4 |
completed | March 7, 2026, 5:34 a.m. |
Created at: March 4, 2026, 7:37 p.m.