Triple
T13447157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LFML |
E320512
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
MRS
MRS is the IATA airport code for Marseille Provence Airport, the main international airport serving Marseille, France.
|
E1041075
|
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: MRS | Statement: [LFML, IATAcode, MRS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MRS Context triple: [LFML, IATAcode, MRS]
-
A.
MRS
MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
-
B.
MRM
MRM is a UN-established system that systematically documents, verifies, and reports grave violations committed against children in armed conflict to support accountability and protection efforts.
-
C.
ma.r.s
ma.r.s is a photographic series by German artist Thomas Ruff that transforms satellite imagery of the Martian surface into large-scale, abstracted landscape works.
-
D.
MRSG
MRSG is a U.S. Marine Corps organization that provides specialized logistical, administrative, and operational support to Marine Raider units within Marine Forces Special Operations Command (MARSOC).
-
E.
M&R
M&R is the commonly used abbreviation for Murray & Roberts, a South African engineering and construction services company.
- 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: MRS Triple: [LFML, IATAcode, MRS]
Generated description
MRS is the IATA airport code for Marseille Provence Airport, the main international airport serving Marseille, France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MRS Target entity description: MRS is the IATA airport code for Marseille Provence Airport, the main international airport serving Marseille, France.
-
A.
MRS
MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
-
B.
MRM
MRM is a UN-established system that systematically documents, verifies, and reports grave violations committed against children in armed conflict to support accountability and protection efforts.
-
C.
ma.r.s
ma.r.s is a photographic series by German artist Thomas Ruff that transforms satellite imagery of the Martian surface into large-scale, abstracted landscape works.
-
D.
MRSG
MRSG is a U.S. Marine Corps organization that provides specialized logistical, administrative, and operational support to Marine Raider units within Marine Forces Special Operations Command (MARSOC).
-
E.
M&R
M&R is the commonly used abbreviation for Murray & Roberts, a South African engineering and construction services company.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaef758b08190b9aa5ec7082cd417 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73998221c8190a2d8982a3da28ec9 |
completed | May 3, 2026, 12:03 p.m. |
| NEDg | Description generation | batch_69f73a598c6c81908420b00b665e3b08 |
completed | May 3, 2026, 12:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f73e0e598c8190b030a45e658a5055 |
completed | May 3, 2026, 12:22 p.m. |
Created at: April 9, 2026, 9:41 p.m.