Triple
T2222339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Shanghai |
E48168
|
entity |
| Predicate | annualCargoThroughput |
P28469
|
FINISHED |
| Object | over 700 million tons |
—
|
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: over 700 million tons | Statement: [Port of Shanghai, annualCargoThroughput, over 700 million tons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: annualCargoThroughput Context triple: [Port of Shanghai, annualCargoThroughput, over 700 million tons]
-
A.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
B.
freightTraffic
chosen
Indicates the movement or volume of goods and cargo being transported, typically via commercial transport networks such as rail, road, sea, or air.
-
C.
peakFreightTrafficRank
Indicates the relative ranking position of an entity based on the highest level of freight traffic it experiences or handles compared to others.
-
D.
annualCapacity
Indicates the maximum amount of output or throughput an entity can produce or handle within a one-year period.
-
E.
servesAsCargoHubFor
Indicates that one entity functions as a central location or facility for handling, consolidating, and distributing cargo associated with another 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_69a88aa1ee708190862c8c378c41e9eb |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc03bfdd48190bfb96ec3e41c22dc |
completed | March 7, 2026, 6:05 a.m. |
| PD | Predicate disambiguation | batch_69abbdac31d8819092d17815e11921e9 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.