Triple
T13019840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TQuery |
E322643
|
entity |
| Predicate | replacedBy |
P101
|
FINISHED |
| Object | TFDQuery |
E322643
|
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: TFDQuery | Statement: [TQuery, replacedBy, TFDQuery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TFDQuery Context triple: [TQuery, replacedBy, TFDQuery]
-
A.
TQuery
chosen
TQuery is a Delphi VCL component that encapsulates SQL query execution and result handling for database applications.
-
B.
EGQuery
EGQuery is an NCBI E-utilities tool that provides a summary of how many records match a given search term across all NCBI databases.
-
C.
FTS
FTS is the commonly used abbreviation for the Financial Tracking Service, a global humanitarian aid financial tracking system managed by the United Nations.
-
D.
Query Service
Query Service is a component of Wikibase that provides a powerful SPARQL-based interface for querying and analyzing structured data stored in Wikibase instances.
-
E.
TFN
TFN is the IATA airport code for Tenerife North Airport, a major airport serving the island of Tenerife in Spain’s Canary Islands.
- 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_69f6c116423881908d0de1e04904fbc3 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:51 p.m.