Triple
T18095578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramón Villeda Morales International Airport |
E433075
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
MHLM
MHLM is the ICAO airport code for Ramón Villeda Morales International Airport, a major international gateway serving San Pedro Sula, Honduras.
|
E1305843
|
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: MHLM | Statement: [Ramón Villeda Morales International Airport, ICAOcode, MHLM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MHLM Context triple: [Ramón Villeda Morales International Airport, ICAOcode, MHLM]
-
A.
HMU
HMU is the commonly used abbreviation for the Hellenic Mediterranean University, a higher education institution in Greece.
-
B.
HLCM
HLCM is the High-level Committee on Management, a senior coordination body within the United Nations system that focuses on management and administrative issues across UN organizations.
-
C.
LHM
LHM is the station code for Lillehammer railway station in Norway.
-
D.
MH
MH is the two-letter ISO 3166-1 alpha-2 country code representing the Republic of the Marshall Islands.
-
E.
MH
MH is the two-letter IATA airline designator used to identify Malaysia Airlines on tickets, timetables, and flight numbers.
- 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: MHLM Triple: [Ramón Villeda Morales International Airport, ICAOcode, MHLM]
Generated description
MHLM is the ICAO airport code for Ramón Villeda Morales International Airport, a major international gateway serving San Pedro Sula, Honduras.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MHLM Target entity description: MHLM is the ICAO airport code for Ramón Villeda Morales International Airport, a major international gateway serving San Pedro Sula, Honduras.
-
A.
HMU
HMU is the commonly used abbreviation for the Hellenic Mediterranean University, a higher education institution in Greece.
-
B.
HLCM
HLCM is the High-level Committee on Management, a senior coordination body within the United Nations system that focuses on management and administrative issues across UN organizations.
-
C.
LHM
LHM is the station code for Lillehammer railway station in Norway.
-
D.
MH
MH is the two-letter ISO 3166-1 alpha-2 country code representing the Republic of the Marshall Islands.
-
E.
MH
MH is the two-letter IATA airline designator used to identify Malaysia Airlines on tickets, timetables, and flight numbers.
- 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_69d8b907d05c819083cc3bd6021089e6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dd1c56848190be0b8c80b30dba6c |
completed | April 19, 2026, 1:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a035d9f808c81909352344ab5d3e672 |
completed | May 12, 2026, 5:04 p.m. |
| NEDg | Description generation | batch_6a036187ba808190a0e1964a09f652a7 |
completed | May 12, 2026, 5:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a036213f46881908c5d7d5468f4f6b3 |
completed | May 12, 2026, 5:23 p.m. |
Created at: April 10, 2026, 10:27 a.m.