Triple
T1983156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sainte-Mère-Église |
E43074
|
entity |
| Predicate | regionDuringWWII |
P27284
|
FINISHED |
| Object | German-occupied France |
—
|
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: German-occupied France | Statement: [Sainte-Mère-Église, regionDuringWWII, German-occupied France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionDuringWWII Context triple: [Sainte-Mère-Église, regionDuringWWII, German-occupied France]
-
A.
statusDuringWorldWarII
Indicates the role, condition, or classification an entity had specifically during the period of World War II.
-
B.
sideInWorldWarII
Indicates that an entity was aligned with or participated on a particular side during World War II.
-
C.
WWIIEvent
Indicates that the event is part of, or directly related to, the historical period and activities of World War II.
-
D.
worldWar
Indicates a large-scale armed conflict involving multiple nations across different regions of the world, typically encompassing numerous battles, alliances, and theaters of war.
-
E.
partOfStateDuringWWII
chosen
Indicates that an entity was territorially or administratively included within a particular state during the period of World War II.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb96f932881908bebfc4176fda7c0 |
completed | March 7, 2026, 5:36 a.m. |
| PD | Predicate disambiguation | batch_69abb798d288819083132cf14605bd02 |
completed | March 7, 2026, 5:28 a.m. |
Created at: March 4, 2026, 7:37 p.m.