Triple
T20005327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lumen Industries |
E494442
|
entity |
| Predicate | department |
P1467
|
FINISHED |
| Object | Macrodata Refinement |
—
|
NE NERFINISHED |
How this triple was built (3 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: Macrodata Refinement | Statement: [Lumen Industries, department, Macrodata Refinement]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macrodata Refinement Context triple: [Lumen Industries, department, Macrodata Refinement]
-
A.
“Macroscopic Data Structure Analysis and Optimization”
“Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
-
B.
Resource Data Analysis Branch
The Resource Data Analysis Branch is a specialized division of the Philippines’ National Mapping and Resource Information Authority responsible for processing, analyzing, and managing geospatial and resource-related data.
-
C.
The Transformation of Data
The Transformation of Data is a notable chapter in R.A. Fisher’s "The Design of Experiments" that discusses methods for mathematically modifying experimental data to meet statistical assumptions and improve analysis.
-
D.
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.
-
E.
Automatic Data Processing
Automatic Data Processing (ADP) is a major American provider of payroll, human resources, and workforce management software and services to businesses worldwide.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Macrodata Refinement Target entity description: Macrodata Refinement is the mysterious, highly controlled office division in the TV series "Severance" where employees perform obscure data-sorting tasks with unclear purpose for the company Lumen Industries.
-
A.
“Macroscopic Data Structure Analysis and Optimization”
“Macroscopic Data Structure Analysis and Optimization” is Chris Lattner’s PhD thesis, in which he develops compiler techniques to analyze and optimize large-scale data structure usage for improved program performance.
-
B.
Resource Data Analysis Branch
The Resource Data Analysis Branch is a specialized division of the Philippines’ National Mapping and Resource Information Authority responsible for processing, analyzing, and managing geospatial and resource-related data.
-
C.
The Transformation of Data
The Transformation of Data is a notable chapter in R.A. Fisher’s "The Design of Experiments" that discusses methods for mathematically modifying experimental data to meet statistical assumptions and improve analysis.
-
D.
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.
-
E.
Automatic Data Processing
Automatic Data Processing (ADP) is a major American provider of payroll, human resources, and workforce management software and services to businesses worldwide.
- F. None of above. chosen
Provenance (2 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a46c748190a141ab5aac6ea250 |
completed | April 20, 2026, 5:25 p.m. |
Created at: April 11, 2026, 3:33 p.m.