Triple
T2435735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johannesburg Stock Exchange |
E52954
|
entity |
| Predicate | clearingModel |
P35399
|
FINISHED |
| Object | central counterparty clearing |
—
|
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: central counterparty clearing | Statement: [Johannesburg Stock Exchange, clearingModel, central counterparty clearing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clearingModel Context triple: [Johannesburg Stock Exchange, clearingModel, central counterparty clearing]
-
A.
clearingSystem
chosen
Indicates that one entity functions as the financial clearing mechanism or infrastructure used to settle transactions for another entity.
-
B.
clearingHouse
Indicates that an entity functions as an intermediary organization that receives, processes, and redistributes transactions, information, or obligations between other parties.
-
C.
clearsProductType
Indicates that one entity removes or resets the specified product type association from another entity.
-
D.
concurrentModel
Indicates that two or more processes, activities, or states occur or are valid at the same time, potentially interacting or overlapping in execution.
-
E.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
- 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_69ab4959bcc0819083246f9fb10439e3 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcebf7cac8190889e6890d72c256c |
completed | March 7, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69abc5ac11b081908ce6a506e81a742a |
completed | March 7, 2026, 6:29 a.m. |
Created at: March 6, 2026, 9:43 p.m.