Triple
T33796327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shuangliu District |
E866083
|
entity |
| Predicate | hasOneOfBusiestAirportsIn |
P20201
|
FINISHED |
| Object | China |
—
|
NE NERFINISHED |
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: China | Statement: [Shuangliu District, hasOneOfBusiestAirportsIn, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOneOfBusiestAirportsIn Context triple: [Shuangliu District, hasOneOfBusiestAirportsIn, China]
-
A.
oneOfBusiestAirportsIn
chosen
Indicates that an airport is among the busiest airports within a specified location or region.
-
B.
busiestAirportIn
Indicates that the subject location contains or is associated with the airport that has the highest level of traffic or activity within that location.
-
C.
isMajorRegionalAirportFor
Indicates that an airport serves as a primary or significant air travel hub for a particular region.
-
D.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or within a given region.
-
E.
isSecondLargestAirportIn
Indicates that an airport is the second largest (by a specified measure, such as passenger traffic or area) among all airports within a given region or jurisdiction.
- 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_69f3498f99f481909cb271f4965a7594 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7b5ccbda481908fe1945c35e36ce8 |
completed | May 3, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c06f5881908f0b98cad6796478 |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 1, 2026, 1:46 a.m.