Triple
T7968018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guangzhou Baiyun International Airport |
E185253
|
entity |
| Predicate | hubFor |
P423
|
FINISHED |
| Object | FedEx Express |
E4793
|
NE 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: FedEx Express | Statement: [Guangzhou Baiyun International Airport, hubFor, FedEx Express]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FedEx Express Context triple: [Guangzhou Baiyun International Airport, hubFor, FedEx Express]
-
A.
FedEx
chosen
FedEx is a global courier delivery services company known for its overnight shipping and pioneering real-time package tracking.
-
B.
United Parcel Service (UPS)
United Parcel Service (UPS) is a global package delivery and supply chain management company known for its extensive logistics network and brown delivery trucks.
-
C.
TNT Express
TNT Express is an international courier and logistics company known for its global parcel delivery and express mail services.
-
D.
United Express
United Express is the regional brand for United Airlines, operating shorter-haul feeder flights to connect passengers to United’s mainline network.
-
E.
DHL
DHL is the Dag Hammarskjöld Library, the United Nations’ main research and information resource center located at its headquarters in New York.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca8297699481909b75a405f01e03af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3bd06ee081908c5080003fb7b8f7 |
completed | March 31, 2026, 3:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccec9e21d881908963dcc38bcc2df0 |
completed | April 1, 2026, 9:59 a.m. |
Created at: March 30, 2026, 5:13 p.m.