Triple
T14617545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bothasig |
E343123
|
entity |
| Predicate | hasNearbySuburb |
P41355
|
FINISHED |
| Object | Table View |
E340739
|
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: Table View | Statement: [Bothasig, hasNearbySuburb, Table View]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Table View Context triple: [Bothasig, hasNearbySuburb, Table View]
-
A.
Table View
chosen
Table View is a coastal suburb of Cape Town, South Africa, known for its beaches and panoramic views of Table Mountain.
-
B.
TTable
TTable is a Delphi VCL data-access component that represents and manipulates an entire database table through a live, table-based dataset interface.
-
C.
tabla
The tabla is a pair of hand-played drums central to North Indian classical and folk music, known for its complex rhythms and tonal versatility.
-
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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb46439b88190a4affcc7ccedab6b |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde16c005c81908b54fcfd4243d820 |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:25 a.m.