Triple
T18893364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assem Allam |
E462147
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Allam Marine |
—
|
NE NERFINISHED |
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: Allam Marine | Statement: [Assem Allam, employer, Allam Marine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allam Marine Context triple: [Assem Allam, employer, Allam Marine]
-
A.
Allam Marine
chosen
Allam Marine is a UK-based manufacturer specializing in the design and production of diesel generator sets and power solutions for industrial and commercial applications.
-
B.
Sealink
Sealink was a major British ferry company that operated passenger and vehicle services across the Irish Sea and English Channel during the 20th century.
-
C.
Evergreen Marine
Evergreen Marine is a major Taiwanese container shipping company known for operating one of the world’s largest fleets of container vessels.
-
D.
Aeromar
Aeromar is a Mexican regional airline that primarily operates domestic and short-haul international flights, with a major operational base in Mexico City.
-
E.
Atlantic Marine
Atlantic Marine is a shipbuilding company known for constructing specialized research and commercial vessels.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c47d392c81909297211c7d7610a1 |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 10, 2026, 11:58 a.m.