Triple
T7774695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turbo |
E179161
|
entity |
| Predicate | officialName |
P66
|
FINISHED |
| Object | Turbo |
E179161
|
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: Turbo | Statement: [Turbo, officialName, Turbo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Turbo Context triple: [Turbo, officialName, Turbo]
-
A.
Turbo
chosen
Turbo is a Colombian port city in the Antioquia Department, located on the Gulf of Urabá and known as a key gateway between the interior of Colombia and the Caribbean Sea.
-
B.
Turbo
Turbo is a 2013 animated sports-comedy film from DreamWorks Animation about a garden snail who gains incredible speed and pursues his dream of racing in the Indianapolis 500.
-
C.
Turbo Express
Turbo Express is the popular nickname for the British Rail Class 166 diesel multiple-unit trains used for regional and commuter services in the UK.
-
D.
Turbo Track
Turbo Track is a high-speed, vertical roller coaster at Ferrari World Abu Dhabi that launches riders through the park’s iconic red roof.
-
E.
Racer
Racer is a classic wooden racing roller coaster located at Kennywood amusement park in Pennsylvania.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c7046331e0819080ec1a5c23c27cd7 |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7f12b888190b10479c3db81cce2 |
completed | March 29, 2026, 6:34 a.m. |
Created at: March 27, 2026, 4:11 p.m.