Triple
T4891565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Czech Technical University in Prague |
E109573
|
entity |
| Predicate | memberOf |
P10
|
FINISHED |
| Object | CESAER |
E28802
|
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: CESAER | Statement: [Czech Technical University in Prague, memberOf, CESAER]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CESAER Context triple: [Czech Technical University in Prague, memberOf, CESAER]
-
A.
CESAER
chosen
CESAER is a European association of leading universities of science and technology that collaborates to advance engineering education, research, and innovation.
-
B.
CESA
CESA is a California state law that protects plant and animal species at risk of extinction by regulating activities that may harm them or their habitats.
-
C.
Cesca
Cesca is a feminine given name, commonly used as a short form of Francesca.
-
D.
Chéserex
Chéserex is a small Swiss municipality in the canton of Vaud, situated near the Jura Mountains above Lake Geneva.
-
E.
Cellese
Cellese is a regional dialect of the Franco-Provençal language traditionally spoken in a specific area of the Franco-Provençal linguistic region.
- 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_69bd440f71348190b99938e59fb7f9a1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e2444dc819088d5562e90d16d9b |
completed | March 20, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be681a3d7881908120dc642af3f58a |
completed | March 21, 2026, 9:42 a.m. |
Created at: March 20, 2026, 1:28 p.m.