Triple
T28002142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frau Cäcilie M. |
E707175
|
entity |
| Predicate | hasFieldOfRelevance |
P162677
|
FINISHED |
| Object | psychoanalysis |
—
|
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: psychoanalysis | Statement: [Frau Cäcilie M., hasFieldOfRelevance, psychoanalysis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFieldOfRelevance Context triple: [Frau Cäcilie M., hasFieldOfRelevance, psychoanalysis]
-
A.
relevanceIn
chosen
Indicates that something is pertinent or applicable within a specified context, scope, or domain.
-
B.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
-
C.
hasManagementRelevance
Indicates that something is pertinent or significant to management activities, decisions, or responsibilities.
-
D.
fieldOfSignificance
Indicates that something holds particular importance, relevance, or impact within a specified domain, context, or area of interest.
-
E.
hasScientificInterestIn
Indicates that one entity holds a scientific curiosity, concern, or research focus directed toward another 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_69ef96b980d88190a753b2f9a978595a |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: April 27, 2026, 7:57 p.m.