Triple
T36678762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upper Germany |
E905615
|
entity |
| Predicate | laterInfluencedRegion |
P202751
|
FINISHED |
| Object | Alsace |
—
|
NE NERFINISHED |
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: Alsace | Statement: [Upper Germany, laterInfluencedRegion, Alsace]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterInfluencedRegion Context triple: [Upper Germany, laterInfluencedRegion, Alsace]
-
A.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
B.
historicalRegionOfInfluence
Indicates that one entity was historically within the geographic, political, or cultural sphere of influence of another entity.
-
C.
hasRegionalInfluenceFrom
Indicates that one entity’s influence, impact, or authority in a region is derived from or shaped by another entity.
-
D.
countryInfluenced
Indicates that one country has exerted a significant political, economic, cultural, or military impact on another country.
-
E.
regionOfCulturalImpact
Indicates the geographic area where an entity’s cultural influence, activities, or effects are most significantly felt or observed.
- 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_69f76e7011dc819082b324f18b756a1b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a00b3d2e0c88190a8bf49576e654f64 |
completed | May 10, 2026, 4:35 p.m. |
| PD | Predicate disambiguation | batch_6a00b376f4e48190a6676779fe02ea9f |
completed | May 10, 2026, 4:33 p.m. |
| PDg | Predicate description generation | batch_6a00b3d23bdc8190b45dd7eefa72ead5 |
completed | May 10, 2026, 4:35 p.m. |
Created at: May 3, 2026, 4:12 p.m.