Triple
T21835594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pfäffikon SZ |
E539110
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object | SZ |
—
|
NE NERFINISHED |
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: SZ | Statement: [Pfäffikon SZ, hasVehicleRegistrationCode, SZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SZ Context triple: [Pfäffikon SZ, hasVehicleRegistrationCode, SZ]
-
A.
SZ
SZ is the official station code used to identify the Berlin U-Bahn station Seestraße.
-
B.
SZ
chosen
SZ is the official abbreviation for the Swiss canton of Schwyz, one of the founding cantons of Switzerland.
-
C.
SZ
SZ is the vehicle registration code used for the Austrian district of Schwaz in the state of Tyrol.
-
D.
SZ
SZ is the vehicle registration code for the German city of Salzgitter in Lower Saxony.
-
E.
S/Z
S/Z is Roland Barthes’s influential structuralist analysis of Balzac’s short story “Sarrasine,” renowned for its detailed demonstration of textual codes and readerly versus writerly texts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c475cda88190987d08f23caebdc1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0a7a7c368819094e0c4548be30798 |
completed | April 28, 2026, 12:27 p.m. |
Created at: April 16, 2026, 6:55 p.m.