Triple
T16988237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobahn A2 |
E412124
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | A2 |
E402883
|
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: A2 | Statement: [Autobahn A2, abbreviation, A2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: A2 Context triple: [Autobahn A2, abbreviation, A2]
-
A.
A2
A2 is a common nickname for Ann Arbor, Michigan, often used by locals and in regional culture.
-
B.
A2
chosen
A2 is a major coastal highway route, commonly designated in several countries as a key road running along or near the shoreline to connect important coastal cities and regions.
-
C.
A22
A22 is a major Portuguese motorway, commonly known as Via do Infante, that runs across the Algarve region in southern Portugal.
-
D.
2A
2A is a state highway designation used for an auxiliary route of Massachusetts Route 2 that serves local traffic through several cities and towns in the state.
-
E.
A3
A3 is a major national highway in Zimbabwe that serves as an important route connecting key towns and regions within the country.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d27cd2048190800a60ae653e11e1 |
completed | April 18, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dc12e308819093e7f8933cdd6ba9 |
completed | May 10, 2026, 7:27 p.m. |
Created at: April 10, 2026, 5:32 a.m.