Triple
T8438533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ecatepec de Morelos |
E199289
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object | MEX |
E346
|
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: MEX | Statement: [Ecatepec de Morelos, hasVehicleRegistrationCode, MEX]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MEX Context triple: [Ecatepec de Morelos, hasVehicleRegistrationCode, MEX]
-
A.
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.
-
B.
Guatimozín
Guatimozín is another name for Cuauhtémoc, the last Aztec emperor who led the defense of Tenochtitlan against the Spanish conquest.
-
C.
The Mexican
The Mexican is a 2001 crime-comedy film starring Brad Pitt and Julia Roberts that blends romance, dark humor, and a quirky road-trip plot centered around a legendary cursed pistol.
-
D.
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.
-
E.
Mexico
Mexico is a landlocked municipality in the province of Pampanga in the Philippines, known for its historical churches and agricultural economy.
- 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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe135657c81908ed8156fbfbef6ec |
completed | March 31, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1d87403c8190b979af4979e43517 |
completed | April 2, 2026, 7:40 a.m. |
Created at: March 30, 2026, 6:08 p.m.