Triple
T10602902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carshalton railway station |
E275794
|
entity |
| Predicate | ticketingSystem |
P3383
|
FINISHED |
| Object | Oyster |
E293568
|
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: Oyster | Statement: [Carshalton railway station, ticketingSystem, Oyster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oyster Context triple: [Carshalton railway station, ticketingSystem, Oyster]
-
A.
Oyster
chosen
Oyster is a contactless smartcard used for paying fares on public transport in London.
-
B.
Mejillones
Mejillones is a coastal Chilean port city on the Pacific Ocean, known for its fishing industry and role in regional maritime trade.
-
C.
Lamut
Lamut is an indigenous Siberian people of northeastern Russia, more commonly known as the Even.
-
D.
Dungeness crab
The Dungeness crab is a large, commercially important crab species native to the Pacific coast of North America, prized for its sweet, tender meat.
-
E.
Kerang
Kerang is a regional town in north-western Victoria, Australia, known as an agricultural and service hub for the surrounding rural communities.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6ded6d698819084f96f46ea941461 |
completed | April 8, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95eaffcd0819098e0a06a731b602f |
completed | April 10, 2026, 8:33 p.m. |
Created at: April 8, 2026, 7:32 p.m.