Triple
T7346432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MyTeksi |
E169391
|
entity |
| Predicate | rebrandedAs |
P65
|
FINISHED |
| Object | GrabTaxi |
E659395
|
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: GrabTaxi | Statement: [MyTeksi, rebrandedAs, GrabTaxi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GrabTaxi Context triple: [MyTeksi, rebrandedAs, GrabTaxi]
-
A.
GrabTaxi
chosen
GrabTaxi is a Southeast Asian ride-hailing and transportation platform that began as MyTeksi in Malaysia before expanding regionally under the Grab brand.
-
B.
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.
-
C.
Uber Pool
Uber Pool is a ride-sharing service from Uber that matches multiple passengers heading in similar directions to share a car and split the fare.
-
D.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
E.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
- 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_69c810d0aebc8190a7274fbcd3fe11ff |
completed | March 28, 2026, 5:33 p.m. |
Created at: March 27, 2026, 3:05 p.m.