Triple
T8718185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weather capital of the world |
E206946
|
entity |
| Predicate | appliedToCityType |
P57766
|
FINISHED |
| Object | university city |
—
|
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: university city | Statement: [Weather capital of the world, appliedToCityType, university city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedToCityType Context triple: [Weather capital of the world, appliedToCityType, university city]
-
A.
appliesToUrbanArea
Indicates that the relationship, rule, or condition is specifically relevant or applicable to an urban area.
-
B.
coversCity
Indicates that one entity extends over, includes, or geographically encompasses the area of a specified city.
-
C.
cityServedType
chosen
Indicates the type or category of city that is served by a given entity (such as a facility, service, or infrastructure).
-
D.
refersToCityWithAttribute
Indicates that one entity refers to a city that possesses a specified attribute or set of attributes.
-
E.
appliedToVehicleType
Indicates that something (such as a rule, restriction, or condition) is specifically applicable to a particular type or category of vehicle.
- 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_69ca83572d4881909bef3be2b578d539 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5cdac6988190b9f9cc1f350aae53 |
completed | March 31, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69cc456e806c819087e7d66ee737f242 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:36 p.m.