Triple
T12773176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baron de Nucingen |
E305299
|
entity |
| Predicate | hasBankingSpecialty |
P106826
|
FINISHED |
| Object | high-risk speculation |
—
|
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: high-risk speculation | Statement: [Baron de Nucingen, hasBankingSpecialty, high-risk speculation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBankingSpecialty Context triple: [Baron de Nucingen, hasBankingSpecialty, high-risk speculation]
-
A.
hasBanking
Indicates that one entity provides or is associated with banking services or facilities for another entity.
-
B.
hasBankType
Indicates that an entity is associated with or classified by a particular type or category of bank.
-
C.
hasBank
Indicates that one entity possesses, is associated with, or is served by a particular bank (such as a financial institution or river bank).
-
D.
hasFinancialInstitution
Indicates that one entity is associated with or linked to a financial institution, such as a bank or similar financial service provider.
-
E.
issuerSpecialization
Indicates the particular field, domain, or area of expertise in which the issuer is specialized.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df5b68481908a5d40516b09be52 |
completed | April 10, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69d96409739881909174ba005a986cb5 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d87078c819083ea724238992204 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:28 p.m.