Triple
T16058658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAG |
E389549
|
entity |
| Predicate | parentCompanyOf |
P254
|
FINISHED |
| Object | MASkargo |
E409252
|
NE FINISHED |
How this triple was built (2 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: MASkargo | Statement: [MAG, parentCompanyOf, MASkargo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MASkargo Context triple: [MAG, parentCompanyOf, MASkargo]
-
A.
MASkargo
chosen
MASkargo is the air cargo division of Malaysia Airlines, providing freight and logistics services across a global network.
-
B.
Maski
Maski is a historic town in Karnataka, India, known for yielding one of the earliest Ashokan rock edicts that explicitly names the Mauryan emperor Ashoka.
-
C.
Massi
Massi is a common Italian diminutive or nickname for the given name Massimiliano.
-
D.
Masmo
Masmo is a residential district in the southern suburbs of Stockholm, Sweden, known for its metro station on the red line and proximity to green areas and Lake Mälaren.
-
E.
Jambojet
Jambojet is a Kenyan low-cost airline that operates domestic and regional flights as a subsidiary of Kenya Airways.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1837729e4819086e7429e0a76b0d7 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbe678fc8190b36737a9cd29691c |
completed | May 10, 2026, 1:14 a.m. |
Created at: April 10, 2026, 4:57 a.m.