Triple
T14307335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minto railway station |
E354731
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object | MTO |
E780659
|
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: MTO | Statement: [Minto railway station, hasStationCode, MTO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MTO Context triple: [Minto railway station, hasStationCode, MTO]
-
A.
MTO
MTO is an abbreviation for the Mediterranean Theater of Operations, the World War II combat zone encompassing Allied and Axis military campaigns around the Mediterranean Sea.
-
B.
MTO
MTO is the provincial government ministry responsible for transportation infrastructure, policy, and services in Ontario, Canada.
-
C.
MTO
chosen
MTO is the National Rail station code for Marton railway station in Middlesbrough, England.
-
D.
MMTO
MMTO is the ICAO airport code for Toluca International Airport, a commercial and cargo airport serving the Toluca and greater Mexico City area in Mexico.
-
E.
MYT
MYT is the three-letter ISO 3166-1 alpha-3 country code assigned to the French overseas department and region of Mayotte.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de85b156b0819083f2bd319deed1b6 |
completed | April 14, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d2c32648190bc8bb26d57df57f5 |
completed | May 8, 2026, 1:32 a.m. |
Created at: April 10, 2026, 1:12 a.m.