Triple
T3030674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marseille Provence Airport |
E82885
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
LFML
LFML is the ICAO airport code for Marseille Provence Airport, a major international airport serving the city of Marseille and the Provence region in southern France.
|
E320512
|
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: LFML | Statement: [Marseille Provence Airport, ICAOcode, LFML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LFML Context triple: [Marseille Provence Airport, ICAOcode, LFML]
-
A.
LFMN
LFMN is the ICAO airport code for Nice Côte d’Azur Airport, a major international airport serving Nice and the French Riviera in southeastern France.
-
B.
LF
LF is the commonly used abbreviation for the Linux Foundation, a nonprofit organization that supports and promotes the development of the Linux kernel and other open-source software projects.
-
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.
LM
LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
-
E.
LTFM
LTFM is the ICAO airport code for Istanbul Airport, the main international airport serving Istanbul, Turkey.
- 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: LFML Triple: [Marseille Provence Airport, ICAOcode, LFML]
Generated description
LFML is the ICAO airport code for Marseille Provence Airport, a major international airport serving the city of Marseille and the Provence region in southern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LFML Target entity description: LFML is the ICAO airport code for Marseille Provence Airport, a major international airport serving the city of Marseille and the Provence region in southern France.
-
A.
LFMN
LFMN is the ICAO airport code for Nice Côte d’Azur Airport, a major international airport serving Nice and the French Riviera in southeastern France.
-
B.
LF
LF is the commonly used abbreviation for the Linux Foundation, a nonprofit organization that supports and promotes the development of the Linux kernel and other open-source software projects.
-
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.
LM
LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
-
E.
LTFM
LTFM is the ICAO airport code for Istanbul Airport, the main international airport serving Istanbul, Turkey.
- 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_69ad8b21a62881908ec5dd4fba4a187c |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9aecd384819085d15f701add7c44 |
completed | March 8, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1debd245c819081eb2dec470f9156 |
completed | March 11, 2026, 9:29 p.m. |
| NEDg | Description generation | batch_69b1dfc77c1881909688d5037682fa01 |
completed | March 11, 2026, 9:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1e03121148190840fc48a50c4ec0e |
completed | March 11, 2026, 9:35 p.m. |
Created at: March 8, 2026, 3:01 p.m.