Triple
T1893764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IG Farben |
E41930
|
entity |
| Predicate | brokenUpInto |
P12217
|
FINISHED |
| Object | Bayer |
E85040
|
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: Bayer | Statement: [IG Farben, brokenUpInto, Bayer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer Context triple: [IG Farben, brokenUpInto, Bayer]
-
A.
Bayer
chosen
Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
-
B.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
C.
Hoechst
Hoechst was a major German chemical and pharmaceutical company that later became part of the conglomerate IG Farben.
-
D.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb7c376208190bbf28504f1aac881 |
completed | March 7, 2026, 5:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3ced4708190ba3d5131039691e8 |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:34 p.m.