Triple
T26570124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anitra Steen |
E666798
|
entity |
| Predicate | sharesBorderWithInCareerContext |
P96605
|
FINISHED |
| Object | Swedish alcohol policy |
—
|
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: Swedish alcohol policy | Statement: [Anitra Steen, sharesBorderWithInCareerContext, Swedish alcohol policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesBorderWithInCareerContext Context triple: [Anitra Steen, sharesBorderWithInCareerContext, Swedish alcohol policy]
-
A.
sharesProfessionWith
Indicates that two entities have the same profession or occupational role.
-
B.
sharesBorderWithInOfficeContext
Indicates that two offices or workspaces are directly adjacent to each other, sharing a common boundary or wall within a workplace layout.
-
C.
sharesBorderWithOffice
Indicates that one entity’s boundary directly adjoins or touches the boundary of an office.
-
D.
sharesBorderWithDepartment
Indicates that one administrative department directly borders or touches the territorial boundary of another department.
-
E.
associatedWithCareerOf
chosen
Indicates a relationship where something is connected or relevant to a person’s professional life, occupation, or career trajectory.
- 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_69ee9cfa21c081909e4e36e087debfc6 |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: April 27, 2026, 1:57 a.m.