Triple
T30626410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dannhauser Local Municipality |
E779590
|
entity |
| Predicate | demographicsType1 |
P25372
|
FINISHED |
| Object | First languages |
—
|
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: First languages | Statement: [Dannhauser Local Municipality, demographicsType1, First languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: demographicsType1 Context triple: [Dannhauser Local Municipality, demographicsType1, First languages]
-
A.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
-
B.
demographicsIn
Indicates that demographic information is associated with or applies within a specified geographic or organizational area.
-
C.
demographicsDescriptor
chosen
Indicates a descriptive attribute or classification that characterizes the demographic properties of an entity or group.
-
D.
demographicsNote
Indicates that there is an associated note or commentary describing demographic-related information about an entity.
-
E.
demographicsLabel
Indicates the categorical demographic group or segment that an entity is associated with or classified under.
- 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_69f224a431548190a44ad9d088dbf91f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68a1a08208190be3da494890e15a9 |
completed | May 2, 2026, 11:34 p.m. |
| PD | Predicate disambiguation | batch_69f6860def1c81909d79e1f088c4b5e5 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:28 p.m.