Triple
T787307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dow Chemical Company |
E16832
|
entity |
| Predicate | mergerWith |
P77
|
FINISHED |
| Object | DuPont |
E37316
|
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: DuPont | Statement: [Dow Chemical Company, mergerWith, DuPont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DuPont Context triple: [Dow Chemical Company, mergerWith, DuPont]
-
A.
DuPont
chosen
DuPont is a major American chemical company historically known for pioneering materials science innovations and playing a key role in U.S. industrial and wartime production.
-
B.
Rohm and Haas
Rohm and Haas is a specialty chemicals company known for producing advanced materials and chemical products used in coatings, electronics, and industrial applications.
-
C.
Du Pont
Du Pont is a prominent American industrial and philanthropic family best known for founding the chemical company E.I. du Pont de Nemours and Company.
-
D.
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.
-
E.
Bayer
Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
- 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_69a4936cb7448190914f5fe4b8d81607 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a78171908190a38a70274bfbebf9 |
completed | March 1, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a6891fb80481909ed2ce30789c945e |
completed | March 3, 2026, 7:09 a.m. |
Created at: March 1, 2026, 7:38 p.m.