Triple
T6891793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biscay |
E159064
|
entity |
| Predicate | ISOCode |
P208
|
FINISHED |
| Object |
ES-BI
ES-BI is the ISO 3166-2 code that designates the province of Biscay in Spain.
|
E627060
|
NE FINISHED |
How this triple was built (4 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: ES-BI | Statement: [Biscay, ISOCode, ES-BI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ES-BI Context triple: [Biscay, ISOCode, ES-BI]
-
A.
BIME Analytics
BIME Analytics is a cloud-based business intelligence and data visualization platform known for enabling companies to analyze and report on customer and operational data.
-
B.
Quick BI
Quick BI is Alibaba Cloud’s business intelligence and data analytics platform that enables users to visualize, analyze, and share insights from their data.
-
C.
Business Intelligence Development Studio
Business Intelligence Development Studio was Microsoft's former integrated development environment for creating SQL Server business intelligence solutions, including SSIS, SSAS, and SSRS projects.
-
D.
Power BI
Power BI is a Microsoft business analytics and data visualization platform used to transform, analyze, and present data through interactive dashboards and reports.
-
E.
Ebixa
Ebixa is a brand-name medication containing memantine, used primarily to treat moderate to severe Alzheimer's disease.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ES-BI Triple: [Biscay, ISOCode, ES-BI]
Generated description
ES-BI is the ISO 3166-2 code that designates the province of Biscay in Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ES-BI Target entity description: ES-BI is the ISO 3166-2 code that designates the province of Biscay in Spain.
-
A.
BIME Analytics
BIME Analytics is a cloud-based business intelligence and data visualization platform known for enabling companies to analyze and report on customer and operational data.
-
B.
Quick BI
Quick BI is Alibaba Cloud’s business intelligence and data analytics platform that enables users to visualize, analyze, and share insights from their data.
-
C.
Business Intelligence Development Studio
Business Intelligence Development Studio was Microsoft's former integrated development environment for creating SQL Server business intelligence solutions, including SSIS, SSAS, and SSRS projects.
-
D.
Power BI
Power BI is a Microsoft business analytics and data visualization platform used to transform, analyze, and present data through interactive dashboards and reports.
-
E.
Ebixa
Ebixa is a brand-name medication containing memantine, used primarily to treat moderate to severe Alzheimer's disease.
- F. None of above. chosen
Provenance (5 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d92ecbdc8190992f9c7f4f33f4c4 |
completed | March 27, 2026, 7:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c748d625908190b3b1f7cfe6360016 |
completed | March 28, 2026, 3:19 a.m. |
| NEDg | Description generation | batch_69c749f7ab5c8190ab823fac27f7484d |
completed | March 28, 2026, 3:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c74a6f828c8190bf0cc56227b1a5b2 |
completed | March 28, 2026, 3:26 a.m. |
Created at: March 27, 2026, 2:24 p.m.