Triple
T22550159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgi–Glashow model |
E557536
|
entity |
| Predicate | containsSubgroup |
P10571
|
FINISHED |
| Object | U(1) |
—
|
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: U(1) | Statement: [Georgi–Glashow model, containsSubgroup, U(1)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: U(1) Context triple: [Georgi–Glashow model, containsSubgroup, U(1)]
-
A.
U(1)
chosen
U(1) is the group of complex numbers with absolute value 1 under multiplication, commonly representing the symmetry group of electromagnetism and other abelian gauge theories.
-
B.
U1
U1 is one of Berlin’s oldest and most central U-Bahn lines, running predominantly east–west through inner-city districts and serving key cultural and nightlife areas.
-
C.
U1
U1 is a major line of the Vienna U-Bahn rapid transit system, running in a north–south direction and connecting key districts across the city.
-
D.
U1
U1 is a rapid transit line of the Frankfurt U-Bahn network in Frankfurt am Main, Germany.
-
E.
U1
U1 is a major line of the Nuremberg U-Bahn rapid transit system, connecting key districts across the Nuremberg metropolitan area.
- 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f7546c4819099942c86ca522a60 |
completed | April 29, 2026, 1:31 a.m. |
Created at: April 16, 2026, 8:52 p.m.