Triple
T43927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Germany |
E862
|
entity |
| Predicate | hadOccupationStatusUntil |
P3070
|
FINISHED |
| Object | 1955 |
—
|
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: 1955 | Statement: [West Germany, hadOccupationStatusUntil, 1955]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadOccupationStatusUntil Context triple: [West Germany, hadOccupationStatusUntil, 1955]
-
A.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
-
B.
retired
Indicates that an entity has permanently stopped working in their former occupation or role, typically after reaching a certain age or service duration.
-
C.
employedApproximately
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
-
D.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
E.
status
Indicates the current condition, state, or standing of an entity within a given context.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24c083ad081909c1122c8fb29efdc |
completed | Feb. 28, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69a24aba9a2c81909f769a8f22e30c92 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24c0794c0819095509d970e05fc0f |
completed | Feb. 28, 2026, 1:59 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.