Triple
T10904552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brighton railway station |
E257532
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object | BTN |
E331322
|
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: BTN | Statement: [Brighton railway station, stationCode, BTN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BTN Context triple: [Brighton railway station, stationCode, BTN]
-
A.
BTN
BTN is the three-letter ISO 3166-1 alpha-3 country code assigned to Bhutan.
-
B.
BTN
chosen
BTN is a U.S. sports television network dedicated primarily to broadcasting collegiate athletics and related programming from the Big Ten Conference.
-
C.
BN
BN is the vehicle registration code used on license plates for the German city of Bonn.
-
D.
BTN2Go
BTN2Go was the Big Ten Network’s dedicated online streaming platform for live and on-demand conference sports content.
-
E.
BAT
BAT is a major multinational tobacco company known for producing and marketing cigarettes and other nicotine products worldwide.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770656c288190828e71600bb0acd4 |
completed | April 9, 2026, 9:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e1553bb88c8190b9730a31977e1dd1 |
completed | April 16, 2026, 9:31 p.m. |
Created at: April 8, 2026, 9:22 p.m.