Triple
T36564650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buchbrunn |
E901939
|
entity |
| Predicate | hasNumberOfOrtsteile |
P104945
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Buchbrunn, hasNumberOfOrtsteile, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfOrtsteile Context triple: [Buchbrunn, hasNumberOfOrtsteile, 1]
-
A.
numberOfStadtkreise
Indicates the quantity of urban districts (Stadtkreise) associated with a given administrative or geographic entity.
-
B.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
C.
hasMunicipalPart
Indicates that an administrative or territorial entity includes a municipality as one of its constituent parts.
-
D.
hasNumberOfSubdistricts
chosen
Indicates the relationship specifying how many subdistricts are associated with a given entity.
-
E.
hasNumberOfComponentCities
Indicates the relationship that specifies how many component cities are contained within or associated with a given entity.
- 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_69f76e6416708190a9754b8c52d4e453 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff1a972bf08190860696ffcd887c0f |
completed | May 9, 2026, 11:29 a.m. |
| PD | Predicate disambiguation | batch_69ff184005d88190bf38283ebc499b28 |
completed | May 9, 2026, 11:19 a.m. |
Created at: May 3, 2026, 4:11 p.m.