Triple
T15174601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morelia International Airport |
E362572
|
entity |
| Predicate | hasICAOcode |
P419
|
FINISHED |
| Object |
MMMM
MMMM is the ICAO airport code for General Francisco J. Mujica International Airport serving Morelia, Michoacán, Mexico.
|
E1142201
|
NE FINISHED |
How this triple was built (4 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: MMMM | Statement: [Morelia International Airport, hasICAOcode, MMMM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MMMM Context triple: [Morelia International Airport, hasICAOcode, MMMM]
-
A.
MMM
MMM is a post-nominal designation signifying membership in the Order of Military Merit, a Canadian honor recognizing exceptional service and devotion by members of the Canadian Armed Forces.
-
B.
MM
MM is a post-nominal abbreviation indicating that a person has been awarded the Military Medal for bravery in battle.
-
C.
MMMY
MMMY is the ICAO airport code for General Mariano Escobedo International Airport serving Monterrey, Mexico.
-
D.
MMP
MMP is a hybrid electoral system that combines single-member district representation with proportional party lists to align a legislature’s overall seat distribution with parties’ share of the vote.
-
E.
M
M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MMMM Triple: [Morelia International Airport, hasICAOcode, MMMM]
Generated description
MMMM is the ICAO airport code for General Francisco J. Mujica International Airport serving Morelia, Michoacán, Mexico.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MMMM Target entity description: MMMM is the ICAO airport code for General Francisco J. Mujica International Airport serving Morelia, Michoacán, Mexico.
-
A.
MMM
MMM is a post-nominal designation signifying membership in the Order of Military Merit, a Canadian honor recognizing exceptional service and devotion by members of the Canadian Armed Forces.
-
B.
MM
MM is a post-nominal abbreviation indicating that a person has been awarded the Military Medal for bravery in battle.
-
C.
MMMY
MMMY is the ICAO airport code for General Mariano Escobedo International Airport serving Monterrey, Mexico.
-
D.
MMP
MMP is a hybrid electoral system that combines single-member district representation with proportional party lists to align a legislature’s overall seat distribution with parties’ share of the vote.
-
E.
M
M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
- F. None of above. chosen
Provenance (5 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0066236d481909e8ac47f496861ad |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec88e7ff481909e3d2b280689aeba |
completed | May 9, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_69fecb7b5bdc81908bb5330bf4035b51 |
completed | May 9, 2026, 5:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fecbeb73f081909695e10aff1b0743 |
completed | May 9, 2026, 5:53 a.m. |
Created at: April 10, 2026, 3:09 a.m.