Triple
T16587931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leopold Ružička |
E403006
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Ciba |
E584144
|
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: Ciba | Statement: [Leopold Ružička, employer, Ciba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ciba Context triple: [Leopold Ružička, employer, Ciba]
-
A.
Ciba-Geigy
chosen
Ciba-Geigy was a major Swiss pharmaceutical and chemical company that became one of the predecessors of Novartis after its merger with Sandoz in 1996.
-
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.
Syntex
Syntex was a pioneering pharmaceutical company best known for its role in developing the first oral contraceptive pill and advancing steroid chemistry.
-
D.
Hoechst
Hoechst was a major German chemical and pharmaceutical company that later became part of the conglomerate IG Farben.
-
E.
Dalmine
Dalmine is an industrial town in northern Italy known for its steel production and manufacturing activities.
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599daa508190a9ed6f64138c0e53 |
completed | April 18, 2026, 10:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007599dcd4819089bbd0569b3d9a12 |
completed | May 10, 2026, 12:10 p.m. |
Created at: April 10, 2026, 5:16 a.m.