Triple
T2222332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Shanghai |
E48168
|
entity |
| Predicate | annualContainerThroughput |
P37127
|
FINISHED |
| Object | over 40 million TEU |
—
|
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 40 million TEU | Statement: [Port of Shanghai, annualContainerThroughput, over 40 million TEU]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: annualContainerThroughput Context triple: [Port of Shanghai, annualContainerThroughput, over 40 million TEU]
-
A.
annualCapacity
Indicates the maximum amount of output or throughput an entity can produce or handle within a one-year period.
-
B.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
C.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
-
D.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
-
E.
annualRidership
Indicates the total number of passengers who use a transportation service over the course of one year.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69abbfe93d7c81909f1b9c1b1e3c7989 |
completed | March 7, 2026, 6:04 a.m. |
Created at: March 4, 2026, 7:47 p.m.