Triple
T16471330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enzkreis |
E400067
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | PF |
E757059
|
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: PF | Statement: [Enzkreis, vehicleRegistrationCode, PF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PF Context triple: [Enzkreis, vehicleRegistrationCode, PF]
-
A.
PF
chosen
PF is the vehicle registration code used on license plates for the German city of Pforzheim.
-
B.
FP
FP is the station code for Floral Park station on the Long Island Rail Road in New York.
-
C.
PW
PW is the abbreviation for "The Professional Web," a term typically referring to the ecosystem of standards, tools, and practices used to build and maintain modern, production-quality websites and web applications.
-
D.
PW
PW is the commonly used nickname of P. W. Botha, the former South African prime minister and state president during the apartheid era.
-
E.
PW
PW is the commonly used abbreviation for the Warsaw University of Technology, one of Poland’s leading technical universities.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dd0d2fc81909b68b5afb00f192f |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f5af4308190bd023624de35027f |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:11 a.m.