Triple
T12816973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tübingen |
E306426
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | TÜ |
E611657
|
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: TÜ | Statement: [Tübingen, vehicleRegistrationCode, TÜ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TÜ Context triple: [Tübingen, vehicleRegistrationCode, TÜ]
-
A.
TÜ
chosen
TÜ is the official vehicle registration code used on license plates for the district of Tübingen in Germany.
-
B.
TU
TU is the international vehicle registration code assigned to Tunisia.
-
C.
TU
TU is the common English abbreviation for Tohoku University, a leading national research university in Sendai, Japan.
-
D.
TU9
TU9 is an alliance of nine leading German Institutes of Technology focused on engineering and natural sciences research and education.
-
E.
Tu
Tu Youyou is a Chinese pharmaceutical chemist and Nobel laureate renowned for discovering the antimalarial drug artemisinin.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9d00088190ac0f5d60e1de7a7c |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ecee33c8190a6bf045731bb9326 |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 9, 2026, 5:31 p.m.