Triple
T2952165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lubrizol |
E79843
|
entity |
| Predicate | businessDivision |
P1467
|
FINISHED |
| Object | Lubrizol Additives |
E79843
|
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: Lubrizol Additives | Statement: [Lubrizol, businessDivision, Lubrizol Additives]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lubrizol Additives Context triple: [Lubrizol, businessDivision, Lubrizol Additives]
-
A.
Lubrizol
chosen
Lubrizol is a specialty chemicals company best known for producing additives and advanced materials used in lubricants, personal care products, and industrial applications.
-
B.
Castrol
Castrol is a global brand of industrial and automotive lubricants, best known for its high-performance motor oils and long-standing involvement in motorsports sponsorship.
-
C.
Lucas Oil
Lucas Oil is an American manufacturer and distributor of automotive oils, lubricants, and additives widely used in consumer, commercial, and motorsports applications.
-
D.
SABIC
SABIC is a major Saudi-based global petrochemicals and plastics manufacturer known as one of the world’s largest chemical companies.
-
E.
BASF
BASF is a major German chemical company and one of the world's largest producers of chemicals and related products.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad98fcea5c8190b7d80de942bcb4f7 |
completed | March 8, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc7edc808190863ec8f99efa3875 |
completed | March 11, 2026, 5:24 a.m. |
Created at: March 8, 2026, 2:57 p.m.