Triple
T13190953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tegel |
E313982
|
entity |
| Predicate | belongsToUrbanDistrictType |
P84910
|
FINISHED |
| Object | outer district of Berlin |
—
|
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: outer district of Berlin | Statement: [Tegel, belongsToUrbanDistrictType, outer district of Berlin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToUrbanDistrictType Context triple: [Tegel, belongsToUrbanDistrictType, outer district of Berlin]
-
A.
belongsToUrbanZone
Indicates that something is located within, or is a part of, a designated urban zone or area.
-
B.
isPartOfUrbanDistrictType
chosen
Indicates that one administrative or geographic unit belongs to, or is classified within, a specific type of urban district.
-
C.
appliesToUrbanAreaType
Indicates that something (such as a rule, measure, or classification) is applicable specifically to a particular type or category of urban area.
-
D.
hasUrbanDistrictCount
Indicates the number of urban districts associated with a given entity.
-
E.
hasUrbanRelation
Indicates a relationship where one entity is connected to another through an urban context, such as city-based location, influence, or interaction.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc6bc108190b5a6a265bf6e9fd4 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:15 p.m.