Triple
T5878366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carl Djerassi |
E130681
|
entity |
| Predicate | citizenship |
P2
|
FINISHED |
| Object | Austria |
E2895
|
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: Austria | Statement: [Carl Djerassi, citizenship, Austria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Austria Context triple: [Carl Djerassi, citizenship, Austria]
-
A.
Austria
chosen
Austria is a landlocked Central European country known for its Alpine landscapes, rich cultural and musical heritage, and status as a prosperous, democratic member of the European Union.
-
B.
Germany and Austria
Germany and Austria are neighboring Central European countries that share historical, cultural, and linguistic ties, including a common use of the German language.
-
C.
Austria-Este
Austria-Este is a cadet branch of the Habsburg-Lorraine dynasty that historically ruled the Duchy of Modena and Reggio and continues as a prominent European noble house.
-
D.
Austria and Slovakia
Austria and Slovakia are neighboring Central European countries that share a border along the Morava River.
-
E.
Styria
Styria is a federal state in southeastern Austria known for its capital Graz, diverse landscapes, and strong industrial and educational sectors.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036327efc8190858e9364cd5d317b |
completed | March 22, 2026, 6:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0e377e1108190b0820f92eab012c2 |
completed | March 23, 2026, 6:53 a.m. |
Created at: March 22, 2026, 3:57 p.m.