Triple
T17028805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leytonstone High Road railway station |
E413135
|
entity |
| Predicate | hasRailcode |
P27071
|
FINISHED |
| Object | LER |
E1246823
|
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: LER | Statement: [Leytonstone High Road railway station, hasRailcode, LER]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LER Context triple: [Leytonstone High Road railway station, hasRailcode, LER]
-
A.
LER
LER is the vehicle registration code assigned to the German island municipality of Borkum.
-
B.
LER
chosen
LER is the National Rail station code for Leytonstone High Road railway station in London.
-
C.
LR
LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
-
D.
LR
LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
-
E.
LR
LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d858448190acfe81f10d83ed4b |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012336cfd481909f93c6ea7c94b49f |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.