Triple
T9590215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korea Exchange |
E231396
|
entity |
| Predicate | index |
P1393
|
FINISHED |
| Object | KOSPI 100 |
E356386
|
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: KOSPI 100 | Statement: [Korea Exchange, index, KOSPI 100]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KOSPI 100 Context triple: [Korea Exchange, index, KOSPI 100]
-
A.
KOSPI
chosen
KOSPI is South Korea’s main stock market index, tracking the performance of large companies listed on the Korea Exchange.
-
B.
KSE-100 Index
The KSE-100 Index is Pakistan’s benchmark stock market index, tracking the performance of the largest and most liquid companies listed on the Pakistan Stock Exchange.
-
C.
KSE-30 Index
The KSE-30 Index is a benchmark stock market index in Pakistan that tracks the performance of 30 of the largest and most liquid companies listed on the Pakistan Stock Exchange.
-
D.
SSE Composite Index
The SSE Composite Index is a major Chinese stock market index that tracks the performance of all stocks listed on the Shanghai Stock Exchange.
-
E.
SSE 50 Index
The SSE 50 Index is a blue-chip stock market index that tracks the performance of 50 of the largest and most liquid companies listed on the Shanghai Stock Exchange.
- 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_69ca8482884481908eccdfdf64d6fbf7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99f32e688190bb13bccfa5031f16 |
completed | April 1, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1791e330881908a6ad31a5bdbccec |
completed | April 4, 2026, 8:48 p.m. |
Created at: March 30, 2026, 8:06 p.m.