Triple
T7346689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GrabBike |
E169396
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | GrabBike |
E169396
|
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: GrabBike | Statement: [GrabBike, brand, GrabBike]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GrabBike Context triple: [GrabBike, brand, GrabBike]
-
A.
GrabBike
chosen
GrabBike is Grab’s on-demand motorcycle ride-hailing service that offers quick, affordable two-wheeled transportation in various Southeast Asian cities.
-
B.
SmartRider
SmartRider is a reusable contactless smart card used for electronic fare payment across Transperth’s public transport network in Western Australia.
-
C.
Ola Bike
Ola Bike is a bike-taxi service operated by Indian ride-hailing company Ola, providing affordable two-wheeler rides for short-distance urban travel.
-
D.
GrabCar
GrabCar is a ride-hailing service under the Grab platform that connects passengers with private car drivers via a mobile app across Southeast Asia.
-
E.
Tabontebike
Tabontebike is a village on the atoll of Abemama in the island nation of Kiribati in the central Pacific Ocean.
- 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_69c68a5878888190968ce4d04db8d69f |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f0f0329c8190a0182e3bf62604e5 |
completed | March 27, 2026, 9:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7fa916ac881909acee8184b71dc85 |
completed | March 28, 2026, 3:58 p.m. |
Created at: March 27, 2026, 3:05 p.m.