Triple
T20549058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMUN |
E504549
|
entity |
| Predicate | icaoRegion |
P34409
|
FINISHED |
| Object | Mexico (MM prefix) |
—
|
NE NERFINISHED |
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: Mexico (MM prefix) | Statement: [MMUN, icaoRegion, Mexico (MM prefix)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mexico (MM prefix) Context triple: [MMUN, icaoRegion, Mexico (MM prefix)]
-
A.
Mexico
chosen
Mexico is a large North American country known for its rich pre-Columbian and colonial history, diverse cultures, and influential cuisine and arts.
-
B.
Mexico
Mexico is a landlocked municipality in the province of Pampanga in the Philippines, known for its historical churches and agricultural economy.
-
C.
MEX
MEX is a major expressway in Malaysia that connects Kuala Lumpur to Putrajaya and Cyberjaya, helping to ease traffic congestion between the capital and its southern suburbs.
-
D.
MEX
MEX is the IATA airport code for Mexico City International Airport, the main international gateway serving Mexico City and one of the busiest airports in Latin America.
-
E.
42 Mexico
42 Mexico is a Mexican campus of the global 42 network, offering tuition-free, peer-to-peer programming education using project-based learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b52c048190952b4d0f430813a3 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a5d56060819090f0e2f2f9957982 |
completed | April 20, 2026, 10:16 p.m. |
Created at: April 16, 2026, 11:38 a.m.