Triple
T22219844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gert Postel |
E549179
|
entity |
| Predicate | fieldOfDeception |
P147344
|
FINISHED |
| Object | medicine |
—
|
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: medicine | Statement: [Gert Postel, fieldOfDeception, medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldOfDeception Context triple: [Gert Postel, fieldOfDeception, medicine]
-
A.
typeOfDeception
Indicates the specific kind or category of deceptive act that one entity employs toward another or in a given context.
-
B.
deceptionMotif
Indicates a relationship where one entity employs or embodies a theme of deceit, trickery, or misleading appearance in relation to another entity or situation.
-
C.
usedMeansOfDeception
Indicates that one entity employed a particular method or tool specifically to deceive another entity.
-
D.
cityOfTheft
Indicates the city where a theft took place or was committed.
-
E.
threatensDeceptionOf
Indicates that one entity poses or communicates a risk of deceiving or misleading another entity.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b8fa3d081908db0a0556b009d8f |
completed | April 28, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
| PDg | Predicate description generation | batch_69e723f65c5c8190a0ee3c539e5d0767 |
completed | April 21, 2026, 7:15 a.m. |
Created at: April 16, 2026, 8:37 p.m.