Triple
T20941338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nantes bus network |
E515727
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | TAN network |
—
|
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: TAN network | Statement: [Nantes bus network, partOf, TAN network]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TAN network Context triple: [Nantes bus network, partOf, TAN network]
-
A.
TAN network
chosen
The TAN network is the public transportation system serving the city of Nantes and its metropolitan area in France, encompassing buses, trams, and related transit services.
-
B.
TRA network
The TRA network is Taiwan’s conventional intercity and regional railway system operated by the Taiwan Railways Administration, connecting major cities and rural areas around the island.
-
C.
Tan network
The Tan network is the public transportation system serving the Nantes metropolitan area in France, encompassing trams, buses, and related services.
-
D.
TNET
TNET is the stock ticker symbol for Telenet Group, a Belgian telecommunications and entertainment services provider.
-
E.
TAN
TAN is the public transportation network serving the city of Nantes and its metropolitan area in western France.
- 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_69e0b4fc13408190b06868df03c5c29b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6f955f0148190ae42278ad5c0f363 |
completed | April 21, 2026, 4:13 a.m. |
Created at: April 16, 2026, 12:50 p.m.