Triple
T12868592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guanajuato International Airport |
E307785
|
entity |
| Predicate | hasCode |
P9567
|
FINISHED |
| Object | MMLO |
E307785
|
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: MMLO | Statement: [Guanajuato International Airport, hasCode, MMLO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MMLO Context triple: [Guanajuato International Airport, hasCode, MMLO]
-
A.
MMLO
chosen
MMLO is the ICAO airport code for Guanajuato International Airport, a major air transport hub serving the León–Guanajuato region in central Mexico.
-
B.
MML
MML is a major inter-city rail route in England connecting London with key cities in the East Midlands and South Yorkshire.
-
C.
MM
MM is a post-nominal abbreviation indicating that a person has been awarded the Military Medal for bravery in battle.
-
D.
MMFA
MMFA is the acronym for the Montgomery Museum of Fine Arts, a prominent art museum in Montgomery, Alabama known for its collections of American art and regional works.
-
E.
MMZ
MMZ is the ICAO airline designator assigned to EuroAtlantic Airways, a Portuguese charter and wet-lease carrier.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9708f510c8190b4c64dc340420e85 |
completed | April 10, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af533d188190b9c816cdc892fe99 |
completed | May 3, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:38 p.m.