Triple
T4082482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carl Krauch |
E87507
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | BASF |
E213163
|
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: BASF | Statement: [Carl Krauch, employer, BASF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BASF Context triple: [Carl Krauch, employer, BASF]
-
A.
BASF
chosen
BASF is a major German chemical company and one of the world's largest producers of chemicals and related products.
-
B.
Dow Chemical Company
Dow Chemical Company is a major American multinational chemical corporation known for producing a wide range of industrial, agricultural, and consumer chemical products.
-
C.
SABIC
SABIC is a major Saudi-based global petrochemicals and plastics manufacturer known as one of the world’s largest chemical companies.
-
D.
Celanese Corporation
Celanese Corporation is a global specialty materials and chemical company known for producing engineered materials, acetyl products, and other advanced polymers for industrial and consumer applications.
-
E.
Ineos
Ineos is a large multinational chemicals and energy company based in the United Kingdom, known for its extensive portfolio of petrochemical, oil, gas, and manufacturing operations worldwide.
- 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_69aed9435cf48190ad1da737c962d19d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc77dab481909bcf197daf2def59 |
completed | March 9, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b562c6456081908cca823ebb13936a |
completed | March 14, 2026, 1:29 p.m. |
Created at: March 9, 2026, 3:39 p.m.