Triple
T9782159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sidcup |
E237400
|
entity |
| Predicate | postcodeArea |
P920
|
FINISHED |
| Object | DA |
E237398
|
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: DA | Statement: [Sidcup, postcodeArea, DA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DA Context triple: [Sidcup, postcodeArea, DA]
-
A.
DA
DA is the official abbreviation for the United States Department of the Army, the federal agency responsible for organizing, training, and equipping the U.S. Army.
-
B.
DA
chosen
DA is a postcode area in southeast England covering parts of south-east London and northwest Kent, including towns such as Dartford and Sidcup.
-
C.
DA
DA is the commonly used abbreviation for the Defence Academy of the United Kingdom, the institution responsible for advanced education and training of the UK’s armed forces and defence personnel.
-
D.
DA
DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
-
E.
Da
Da was the personal given name of Emperor Zhang, a ruler of the Eastern Han dynasty in ancient China.
- 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_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda1b23cb88190b458ab18d5f7f493 |
completed | April 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c41b31b08190937f374c2d51aa1b |
completed | April 5, 2026, 2:08 a.m. |
Created at: March 30, 2026, 8:27 p.m.