Triple
T4017456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoechst Administration Building |
E91197
|
entity |
| Predicate | originalClient |
P9839
|
FINISHED |
| Object | Hoechst AG |
E213164
|
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: Hoechst AG | Statement: [Hoechst Administration Building, originalClient, Hoechst AG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hoechst AG Context triple: [Hoechst Administration Building, originalClient, Hoechst AG]
-
A.
Hoechst
chosen
Hoechst was a major German chemical and pharmaceutical company that later became part of the conglomerate IG Farben.
-
B.
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.
-
C.
Preussag AG
Preussag AG was a former German industrial and mining conglomerate that transformed in the 1990s into a tourism-focused company, eventually becoming today’s TUI Group.
-
D.
BASF
BASF is a major German chemical company and one of the world's largest producers of chemicals and related products.
-
E.
Lonza
Lonza is a global Swiss-based life sciences company specializing in pharmaceutical, biotech, and nutrition products and services, particularly in contract development and manufacturing.
- 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_69aed9618b04819081750d979d2af098 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa85ac881909e46fb3d76a794af |
completed | March 9, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c792d948190aed89f2a52d65688 |
completed | March 14, 2026, 11:54 a.m. |
Created at: March 9, 2026, 3:35 p.m.