Triple
T15315290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TD postcode area |
E366138
|
entity |
| Predicate | hasPostcodeDistrict |
P961
|
FINISHED |
| Object | TD8 |
E366138
|
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: TD8 | Statement: [TD postcode area, hasPostcodeDistrict, TD8]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TD8 Context triple: [TD postcode area, hasPostcodeDistrict, TD8]
-
A.
TD
chosen
TD is a UK postcode area covering parts of the Scottish Borders and northern England, including towns such as Galashiels and Berwick-upon-Tweed.
-
B.
TD
TD is the stock ticker symbol for The Toronto-Dominion Bank, one of Canada’s largest multinational banking and financial services institutions.
-
C.
TD
TD is the two-letter ISO 3166-1 alpha-2 country code assigned to Chad.
-
D.
TD3
TD3 (Twin Delayed Deep Deterministic Policy Gradient) is an off-policy deep reinforcement learning algorithm that improves upon DDPG by reducing overestimation bias and stabilizing training for continuous control tasks.
-
E.
T8
T8 is a Sydney Trains suburban rail line serving the Airport, Inner South, and South Western suburbs of Sydney, Australia.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd050108190a584543cb93943a4 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8a688a48190848eb7f065aba146 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.