Triple
T7064848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blaustein |
E164318
|
entity |
| Predicate | hasRailwayStation |
P918
|
FINISHED |
| Object | Blaustein station |
E267218
|
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: Blaustein station | Statement: [Blaustein, hasRailwayStation, Blaustein station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blaustein station Context triple: [Blaustein, hasRailwayStation, Blaustein station]
-
A.
Blaustein station
chosen
Blaustein station is a local railway stop serving the town of Blaustein in the state of Baden-Württemberg, Germany.
-
B.
Atco station
Atco station is a New Jersey Transit rail stop in Atco, New Jersey, serving passengers on the Atlantic City Line between Philadelphia and Atlantic City.
-
C.
Las Parcelas station
Las Parcelas station is a stop on Santiago’s Metro system serving Line 5 in the city’s western sector.
-
D.
Paulina station
Paulina station is an elevated Chicago 'L' rapid transit stop in the Lakeview neighborhood, served by the Brown Line.
-
E.
Snyder station
Snyder station is an underground rapid transit stop on SEPTA’s Broad Street Line serving South Philadelphia.
- 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_69c688796c148190adb2f1596f595f22 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e45e80e08190bb1a79a6026d2cd5 |
completed | March 27, 2026, 8:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7944b4a60819089e565cb895c432f |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 2:39 p.m.