Triple
T12726384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altreich and annexed territories |
E304117
|
entity |
| Predicate | territorialChange |
P1701
|
FINISHED |
| Object | dissolution after German defeat in 1945 |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: dissolution after German defeat in 1945 | Statement: [Altreich and annexed territories, territorialChange, dissolution after German defeat in 1945]
Provenance (2 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96415ebe48190ae935bc3a9b00f65 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:25 p.m.