Triple
T9874580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Pudsey railway station |
E240041
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Pudsey |
E372053
|
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: Pudsey | Statement: [New Pudsey railway station, serves, Pudsey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pudsey Context triple: [New Pudsey railway station, serves, Pudsey]
-
A.
Pudsey
chosen
Pudsey is a market town in West Yorkshire, England, situated between Leeds and Bradford.
-
B.
Paddington Bear
Paddington Bear is a beloved fictional bear from Peru who wears a duffle coat and hat, loves marmalade sandwiches, and stars in a long-running series of children's books and film adaptations set in London.
-
C.
Teddy
Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
-
D.
Teddy
Teddy is a recurring character on the animated TV show "Bob's Burgers," known as the Belcher family's loyal but somewhat bumbling handyman and regular customer.
-
E.
Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f89fa081908b58956902c193cf |
completed | April 2, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eae5a31c81908085f637fbbff6d2 |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:37 p.m.