Triple
T20941527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TAN public transport network |
E515732
|
entity |
| Predicate | hasBrandName |
P40804
|
FINISHED |
| Object | TAN |
—
|
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 | Statement: [TAN public transport network, hasBrandName, TAN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TAN Context triple: [TAN public transport network, hasBrandName, TAN]
-
A.
TAN
chosen
TAN is the public transportation network serving the city of Nantes and its metropolitan area in western France.
-
B.
TANU
TANU was the principal nationalist political party in Tanganyika that led the country to independence under the leadership of Julius Nyerere.
-
C.
TANAPA
TANAPA is the government agency responsible for managing and conserving Tanzania’s national parks and their wildlife.
-
D.
TATN
TATN is the stock ticker symbol for Tatneft, a major Russian oil and gas company.
-
E.
Tan
Tan is a surname and given name commonly found in various East and Southeast Asian cultures, often representing a romanization of different Chinese family names.
- 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.