Triple
T11267943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1 Line |
E266735
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Othello station |
E766132
|
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: Othello station | Statement: [1 Line, hasStation, Othello station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Othello station Context triple: [1 Line, hasStation, Othello station]
-
A.
Othello Station
chosen
Othello Station is a light rail transit stop in Seattle’s Rainier Valley neighborhood, serving the Link light rail system.
-
B.
Legarda station
Legarda station is an elevated rapid transit stop on Manila’s LRT Line 2 serving the Sampaloc area and nearby universities.
-
C.
Cicero station
Cicero station is a commuter rail stop in Cicero, Illinois, serving passengers on Metra’s BNSF Railway Line between Chicago and its western suburbs.
-
D.
La Estrella station
La Estrella station is the southern terminal station of Line A of the Medellín Metro system in Colombia.
-
E.
Bellavista station
Bellavista station is a passenger rail stop on the Valparaíso Metro system serving the coastal city of Valparaíso, Chile.
- 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_69d6aac8c2f48190ad0596f1f89f0470 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e94f60d48190bc925c3cb88641a8 |
completed | April 9, 2026, 6 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ccd52c20819093e03bba2fd359b7 |
completed | April 19, 2026, 12:38 p.m. |
Created at: April 8, 2026, 9:31 p.m.