Triple
T70636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SF |
E1413
|
entity |
| Predicate | cityType |
P749
|
FINISHED |
| Object | consolidated city-county |
—
|
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: consolidated city-county | Statement: [SF, cityType, consolidated city-county]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityType Context triple: [SF, cityType, consolidated city-county]
-
A.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
B.
city1
Indicates that the subject is classified as a city.
-
C.
urbanAreaType
chosen
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
D.
servesAsFocusCityFor
Indicates that a city functions as a primary or designated focus city for an airline, organization, or transportation network, typically hosting significant but not hub-level operations or activities.
-
E.
largestCity
Indicates that one city is the most populous or significant urban center within a specified region or 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eaa0df88190add55579b2b9fd02 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.