Triple
T15887517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strada Statale 163 Amalfitana |
E385227
|
entity |
| Predicate | maintainedBy |
P86
|
FINISHED |
| Object | ANAS |
E535717
|
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: ANAS | Statement: [Strada Statale 163 Amalfitana, maintainedBy, ANAS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ANAS Context triple: [Strada Statale 163 Amalfitana, maintainedBy, ANAS]
-
A.
ANAS
ANAS is the primary state research institution in Azerbaijan, overseeing and coordinating scientific activities and academic research across the country.
-
B.
ANAS
chosen
ANAS is Italy’s national road agency responsible for the construction, maintenance, and management of much of the country’s road and motorway network.
-
C.
Ansen
Ansen is a small village in the Dutch province of Drenthe, located within the municipality of De Wolden.
-
D.
ANE
ANE is the IATA airport code for Angers – Loire Airport, a regional airport serving the Angers area in western France.
-
E.
ANE
ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
- 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1561b6fb48190adcf8277e1895fda |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa95791a48190abc79a6906672098 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:51 a.m.