Triple
T12281819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DDD |
E292732
|
entity |
| Predicate | acronymFor |
P590
|
FINISHED |
| Object | Data Display Debugger |
E974231
|
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: Data Display Debugger | Statement: [DDD, acronymFor, Data Display Debugger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Data Display Debugger Context triple: [DDD, acronymFor, Data Display Debugger]
-
A.
Data Display Debugger
chosen
Data Display Debugger is a software debugging tool designed to visually inspect and analyze data structures and program state during execution.
-
B.
DBG
DBG is the Indian Railways station code for Darbhanga Junction, a major railway station in the city of Darbhanga in Bihar, India.
-
C.
Turbo Debugger
Turbo Debugger is a DOS-based source-level debugger from Borland, commonly used alongside Turbo C++ for debugging C and C++ programs.
-
D.
DataView
DataView is a low-level JavaScript interface that provides flexible, byte-level read and write access to the contents of an ArrayBuffer, supporting multiple numeric types and endianness.
-
E.
DataView
DataView is ML.NET’s core, schema-aware tabular data abstraction used to efficiently represent and process datasets for machine learning pipelines.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cf2b09c81908a11581d33f65be0 |
completed | April 10, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a97614c8190b67e07df3e424e32 |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:52 p.m.