Triple
T6522850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterloo–Reading line |
E151226
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Earley |
E283653
|
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: Earley | Statement: [Waterloo–Reading line, hasStation, Earley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Earley Context triple: [Waterloo–Reading line, hasStation, Earley]
-
A.
Earley
chosen
Earley is a suburban town in Berkshire, England, situated near Reading and known for its residential character and proximity to major transport links.
-
B.
Earle
Earle is the middle name of Gordon E. Moore, the co-founder of Intel and originator of Moore’s Law.
-
C.
Egan
Egan is a surname of Irish origin borne by various notable individuals, including the American novelist Jennifer Egan.
-
D.
Yates
Yates is a surname of English origin borne by various notable individuals across literature, politics, sports, and other fields.
-
E.
Backus
Backus is a surname most notably associated with John Backus, the American computer scientist who led the development of the Fortran programming language.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ad95c2c88190b800aaaa73f99210 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e416e884819099c788cc3a814414 |
completed | March 27, 2026, 8:09 p.m. |
Created at: March 27, 2026, 1:45 p.m.