Triple
T9441410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Navigo |
E227653
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Navigo Easy |
E227653
|
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: Navigo Easy | Statement: [Navigo, hasVariant, Navigo Easy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Navigo Easy Context triple: [Navigo, hasVariant, Navigo Easy]
-
A.
Navigo
chosen
Navigo is the contactless smart card ticketing system used for public transportation across the Île-de-France region, including Paris.
-
B.
Nautica
Nautica is an American lifestyle brand best known for its nautical-inspired apparel and accessories.
-
C.
Simoa
Simoa is a river in southeastern Norway that flows through Buskerud county before joining the larger Drammenselva river system.
-
D.
Avgo
Avgo is a notable summit of Mount Pangaion in northern Greece, known for its rugged terrain and scenic views.
-
E.
Voyager KC2
Voyager KC2 is an Airbus A330-based multi-role tanker transport aircraft used by the Royal Air Force for air-to-air refuelling and strategic airlift operations.
- 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_69ca843884488190ad6cbe0153088234 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7ee4f4a08190ada5ee14fec2b822 |
completed | April 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1105dc6b48190bd6c7d932d9f48d5 |
completed | April 4, 2026, 1:21 p.m. |
Created at: March 30, 2026, 7:50 p.m.