Triple
T13019839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TQuery |
E322643
|
entity |
| Predicate | replacedBy |
P101
|
FINISHED |
| Object | TADOQuery |
E1014698
|
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: TADOQuery | Statement: [TQuery, replacedBy, TADOQuery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TADOQuery Context triple: [TQuery, replacedBy, TADOQuery]
-
A.
TADOQuery
chosen
TADOQuery is a Delphi database component that executes SQL queries using ADO to access and manipulate data from various relational databases.
-
B.
TQuery
TQuery is a Delphi VCL component that encapsulates SQL query execution and result handling for database applications.
-
C.
TAD
TAD is the OECD’s Trade and Agriculture Directorate, which develops international policies and analysis on global trade, agriculture, and related economic issues.
-
D.
TAD
TAD is an acronym commonly used to refer to a Tax Allocation District, a designated area where future tax revenues are used to finance redevelopment and public improvements.
-
E.
QTA
QTA is the official railway station code for Quetta Railway Station in Pakistan’s railway network.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecf21bc819082fb512bc479b4be |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbc98e10819091d71198bca1ac12 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:51 p.m.