Triple
T4219395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rohm and Haas |
E94301
|
entity |
| Predicate | hasMajorCustomerIndustry |
P927
|
FINISHED |
| Object | automotive industry |
—
|
LITERAL 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: automotive industry | Statement: [Rohm and Haas, hasMajorCustomerIndustry, automotive industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorCustomerIndustry Context triple: [Rohm and Haas, hasMajorCustomerIndustry, automotive industry]
-
A.
hasMajorBusinessLine
Indicates that an entity conducts a primary or significant line of business in a specified area, sector, or activity.
-
B.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
C.
hasMajorEmployer
Indicates that an entity has a primary or most significant employer with which it is chiefly affiliated for work or occupation.
-
D.
hasMajorOrganization
Indicates that an entity is associated with or primarily represented by a major organization.
-
E.
majorCustomer
chosen
Indicates that one entity is a primary or high-value customer of another entity, typically contributing a significant portion of business or revenue.
- F. None of above.
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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e4bf6088190926b982039a12079 |
completed | March 12, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69b347f1d7b48190bd8974c03c7dc937 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:04 p.m.