Triple
T3565370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monroe Regional Airport |
E75434
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
MLU
MLU is the IATA airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
|
E369376
|
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: MLU | Statement: [Monroe Regional Airport, IATAcode, MLU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MLU Context triple: [Monroe Regional Airport, IATAcode, MLU]
-
A.
MML
MML is a major inter-city rail route in England connecting London with key cities in the East Midlands and South Yorkshire.
-
B.
LM
LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
-
C.
LM
LM is the Apollo Lunar Module, the spacecraft used by NASA during the Apollo program to land astronauts on the Moon and return them to lunar orbit.
-
D.
ML
ML is a post-nominal honorific indicating a recipient of Papua New Guinea’s Order of Logohu, a national order of merit.
-
E.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
- 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: MLU Triple: [Monroe Regional Airport, IATAcode, MLU]
Generated description
MLU is the IATA airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MLU Target entity description: MLU is the IATA airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
-
A.
MML
MML is a major inter-city rail route in England connecting London with key cities in the East Midlands and South Yorkshire.
-
B.
LM
LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
-
C.
LM
LM is the Apollo Lunar Module, the spacecraft used by NASA during the Apollo program to land astronauts on the Moon and return them to lunar orbit.
-
D.
ML
ML is a post-nominal honorific indicating a recipient of Papua New Guinea’s Order of Logohu, a national order of merit.
-
E.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
- 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_69ad85d512708190829c8b2d3a2ccfb8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0a8f6288190928479f5bea32245 |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bbacbb1081908fc57168a8fc3ade |
completed | March 13, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69b3bf78d6a881908d5bcc4ae50a76e5 |
completed | March 13, 2026, 7:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3f5adac0481908b9053585c317be0 |
completed | March 13, 2026, 11:31 a.m. |
Created at: March 8, 2026, 3:21 p.m.