Triple
T29816067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helmut Dantine |
E757106
|
entity |
| Predicate | warRelatedBackground |
P121218
|
FINISHED |
| Object | anti-Nazi refugee from Austria |
—
|
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: anti-Nazi refugee from Austria | Statement: [Helmut Dantine, warRelatedBackground, anti-Nazi refugee from Austria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: warRelatedBackground Context triple: [Helmut Dantine, warRelatedBackground, anti-Nazi refugee from Austria]
-
A.
warRelatedSite
Indicates a site that is associated with, used in, or significantly affected by a war or armed conflict.
-
B.
militaryConflictInBackstory
chosen
Indicates that a character or entity has a history involving a military conflict that occurred prior to the main events or timeline being described.
-
C.
stateDuringWar
Indicates that a state or condition exists specifically in the context of, or for the duration of, a war or armed conflict.
-
D.
statusDuringWar
Indicates the role, condition, or classification an entity held specifically during a period of war.
-
E.
warDepicted
Indicates that a work or representation portrays, illustrates, or otherwise depicts a particular war or armed conflict.
- 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_69f2245701c88190ad42415a0956c4ed |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f675637b0c81908fca0623b5feb312 |
completed | May 2, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:26 p.m.