Triple
T19102036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shipki La Indo–China trade point |
E467556
|
entity |
| Predicate | tradeScope |
P57403
|
FINISHED |
| Object | limited |
—
|
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: limited | Statement: [Shipki La Indo–China trade point, tradeScope, limited]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tradeScope Context triple: [Shipki La Indo–China trade point, tradeScope, limited]
-
A.
tradeAspect
Indicates a relationship where one entity is associated with a specific aspect, feature, or dimension of a trade or commercial transaction.
-
B.
tradeFocus
Indicates a primary emphasis on or specialization in a particular type of trade, transaction, or commercial activity within the relationship or context.
-
C.
tradeNetworkScope
chosen
Indicates the extent or boundaries within which trade relationships or exchanges are conducted or recognized.
-
D.
tradeHubWith
Indicates that one entity functions as a central location or node for trading activities in connection with another entity.
-
E.
tradeNetwork
Indicates a relationship where entities are connected through the exchange of goods, services, or resources within an ongoing system of trade.
- 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_69d8dd05ac4c8190b1967d8f97f3fb2f |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e36e9bfc8190bbaccab169394d99 |
completed | April 20, 2026, 8:27 a.m. |
| PD | Predicate disambiguation | batch_69e4b9ac41848190afd0f33b42cebe99 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:04 p.m.