Triple
T780387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt am Main |
E16481
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | F |
E5075
|
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: F | Statement: [Frankfurt am Main, vehicleRegistrationCode, F]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: F Context triple: [Frankfurt am Main, vehicleRegistrationCode, F]
-
A.
F
chosen
F is the New York Stock Exchange ticker symbol for Ford Motor Company, the American multinational automaker known for mass-producing automobiles and pioneering assembly line manufacturing.
-
B.
FA
FA is a Chilean left-wing political coalition known for uniting various progressive parties and movements to challenge the country’s traditional political blocs.
-
C.
BF
BF is the IATA airline designator for French Bee, a French low-cost long-haul carrier.
-
D.
FC
FC is the standard abbreviation for Fibre Channel, a high-speed network technology primarily used to connect computer data storage in storage area networks.
-
E.
FÜ
FÜ is the vehicle registration code used on license plates for the city of Fürth in Bavaria, Germany.
- 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_69a4936ad1fc81908f190208059ccf78 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a751ea3481908a622d5255249883 |
completed | March 1, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a66d9dd9b48190ba7fae75db01c114 |
completed | March 3, 2026, 5:11 a.m. |
Created at: March 1, 2026, 7:37 p.m.