Triple
T16972915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madrid metropolitan area |
E411731
|
entity |
| Predicate | hasMotorway |
P385
|
FINISHED |
| Object | A-3 |
E1070569
|
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: A-3 | Statement: [Madrid metropolitan area, hasMotorway, A-3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: A-3 Context triple: [Madrid metropolitan area, hasMotorway, A-3]
-
A.
A-3
chosen
The A-3 is a major Spanish motorway that connects Madrid with Valencia and the eastern Mediterranean coast.
-
B.
A3
A3 is a major national highway in Zimbabwe that serves as an important route connecting key towns and regions within the country.
-
C.
A3
A3 is a major Swiss motorway that runs across the country’s north, connecting key cities and regions as part of the national highway network.
-
D.
A3
A3 is the U.S. Air Force headquarters directorate responsible for overseeing and directing operational planning, readiness, and employment of air and space forces.
-
E.
A3
A3 is a major German autobahn that runs across several federal states, serving as an important east–west transport corridor.
- 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_69e3d0ae47f08190a13e98d20aba7f16 |
completed | April 18, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d4738fbc819099e8281ebc777091 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:31 a.m.