Triple
T15238408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malaysia LNG complex in Bintulu |
E364189
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
MLNG Tiga
MLNG Tiga is one of the liquefied natural gas production trains operated within the Malaysia LNG complex in Bintulu, Sarawak.
|
E1147681
|
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: MLNG Tiga | Statement: [Malaysia LNG complex in Bintulu, hasComponent, MLNG Tiga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MLNG Tiga Context triple: [Malaysia LNG complex in Bintulu, hasComponent, MLNG Tiga]
-
A.
MLNG Satu
MLNG Satu is one of the liquefied natural gas production trains operated within the Malaysia LNG complex in Bintulu, Sarawak.
-
B.
MLNG Dua
MLNG Dua is one of the liquefied natural gas production trains within the Malaysia LNG complex in Bintulu, Sarawak, contributing to Malaysia’s LNG export capacity.
-
C.
3rd MLG
3rd MLG is a United States Marine Corps logistics unit that provides supply, maintenance, transportation, and support services to Marine forces, primarily in the Asia-Pacific region.
-
D.
Manggala
Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
-
E.
MLN
MLN is the IATA airport code for Melilla Airport, which serves the Spanish autonomous city of Melilla on the north coast of Africa.
- 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: MLNG Tiga Triple: [Malaysia LNG complex in Bintulu, hasComponent, MLNG Tiga]
Generated description
MLNG Tiga is one of the liquefied natural gas production trains operated within the Malaysia LNG complex in Bintulu, Sarawak.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MLNG Tiga Target entity description: MLNG Tiga is one of the liquefied natural gas production trains operated within the Malaysia LNG complex in Bintulu, Sarawak.
-
A.
MLNG Satu
MLNG Satu is one of the liquefied natural gas production trains operated within the Malaysia LNG complex in Bintulu, Sarawak.
-
B.
MLNG Dua
MLNG Dua is one of the liquefied natural gas production trains within the Malaysia LNG complex in Bintulu, Sarawak, contributing to Malaysia’s LNG export capacity.
-
C.
3rd MLG
3rd MLG is a United States Marine Corps logistics unit that provides supply, maintenance, transportation, and support services to Marine forces, primarily in the Asia-Pacific region.
-
D.
Manggala
Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
-
E.
MLN
MLN is the IATA airport code for Melilla Airport, which serves the Spanish autonomous city of Melilla on the north coast of Africa.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007da7e988190925a9b67b8070bc7 |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5efb0108190b3b45e9917721354 |
completed | May 9, 2026, 7:44 a.m. |
| NEDg | Description generation | batch_69fee9a9fe5081909c941e5a40cf1203 |
completed | May 9, 2026, 8 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feea73ba7481909386ac23e164bec4 |
completed | May 9, 2026, 8:04 a.m. |
Created at: April 10, 2026, 3:12 a.m.