Triple
T2711896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mobil Oil Nigeria |
E59880
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Mobil |
E88383
|
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: Mobil | Statement: [Mobil Oil Nigeria, brand, Mobil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mobil Context triple: [Mobil Oil Nigeria, brand, Mobil]
-
A.
Mobil
chosen
Mobil is a major American oil company and fuel brand that became part of ExxonMobil after a 1999 merger.
-
B.
Opel
Opel is a German automobile manufacturer known for producing a wide range of passenger cars and light commercial vehicles for the European market.
-
C.
Jeep
Jeep is an American automotive marque best known for its rugged sport utility vehicles and off-road capable 4x4s.
-
D.
Ford
Ford is a common English surname borne by numerous notable individuals, including U.S. President Gerald Ford.
-
E.
Niva
Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
- 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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda90d5a88190bcb6caefd8f2dd1b |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf83e37c8190b7a7815f8acf3a4b |
completed | March 10, 2026, 5:43 a.m. |
Created at: March 6, 2026, 9:55 p.m.