Triple

T1688602
Position Surface form Disambiguated ID Type / Status
Subject Ola Cabs E36498 entity
Predicate offersProduct P10882 FINISHED
Object Ola Micro
Ola Micro is a budget-friendly ride option from Indian ride-hailing company Ola, offering low-cost cab services for short-distance urban travel.
 E191356 NE FINISHED

How this triple was built (4 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: Ola Micro | Statement: [Ola Cabs, offersProduct, Ola Micro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ola Micro
Context triple: [Ola Cabs, offersProduct, Ola Micro]
  • A. Mikrolimano
    Mikrolimano is a picturesque small harbor and marina in Piraeus, Greece, known for its waterfront tavernas, seafood restaurants, and vibrant nightlife.
  • B. Ovi
    Ovi is the widely recognized nickname of Alex Ovechkin, the prolific Russian goal-scorer and NHL superstar.
  • C. Wuling Hongguang MINI EV
    The Wuling Hongguang MINI EV is a popular ultra-compact, low-cost electric city car from China that has become known for making electric mobility widely accessible.
  • D. Moto E
    Moto E is a line of budget-friendly Android smartphones produced by Motorola Mobility, known for offering essential features at low cost.
  • E. Fitel
    Fitel was a financial technology startup where Jeff Bezos worked early in his career, before joining D. E. Shaw and later founding Amazon.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ola Micro
Triple: [Ola Cabs, offersProduct, Ola Micro]
Generated description
Ola Micro is a budget-friendly ride option from Indian ride-hailing company Ola, offering low-cost cab services for short-distance urban travel.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ola Micro
Target entity description: Ola Micro is a budget-friendly ride option from Indian ride-hailing company Ola, offering low-cost cab services for short-distance urban travel.
  • A. Mikrolimano
    Mikrolimano is a picturesque small harbor and marina in Piraeus, Greece, known for its waterfront tavernas, seafood restaurants, and vibrant nightlife.
  • B. Ovi
    Ovi is the widely recognized nickname of Alex Ovechkin, the prolific Russian goal-scorer and NHL superstar.
  • C. Wuling Hongguang MINI EV
    The Wuling Hongguang MINI EV is a popular ultra-compact, low-cost electric city car from China that has become known for making electric mobility widely accessible.
  • D. Moto E
    Moto E is a line of budget-friendly Android smartphones produced by Motorola Mobility, known for offering essential features at low cost.
  • E. Fitel
    Fitel was a financial technology startup where Jeff Bezos worked early in his career, before joining D. E. Shaw and later founding Amazon.
  • F. None of above. chosen

Provenance (5 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_69a886151508819084fa7f1ce6e05577 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6296655c8190835ec0d20f7460ca completed March 6, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad7992792081909af4312ae8a448a2 completed March 8, 2026, 1:28 p.m.
NEDg Description generation batch_69ad7ab4aca48190936384bfa1cdeccf completed March 8, 2026, 1:33 p.m.
NED2 Entity disambiguation (via description) batch_69ad7b34c3248190bb93769e55df7189 completed March 8, 2026, 1:35 p.m.
Created at: March 4, 2026, 7:29 p.m.