Triple
T460405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyft |
E7323
|
entity |
| Predicate | hasProgram |
P178
|
FINISHED |
| Object |
Lyft Pink
Lyft Pink is Lyft’s paid membership program that offers riders benefits like discounted fares, priority support, and other perks on the Lyft platform.
|
E58444
|
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: Lyft Pink | Statement: [Lyft, hasProgram, Lyft Pink]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyft Pink Context triple: [Lyft, hasProgram, Lyft Pink]
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
B.
Uber Green
Uber Green is an eco-focused ride option from Uber that connects riders with drivers using low-emission or electric vehicles.
-
C.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
-
D.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
-
E.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
- 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: Lyft Pink Triple: [Lyft, hasProgram, Lyft Pink]
Generated description
Lyft Pink is Lyft’s paid membership program that offers riders benefits like discounted fares, priority support, and other perks on the Lyft platform.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lyft Pink Target entity description: Lyft Pink is Lyft’s paid membership program that offers riders benefits like discounted fares, priority support, and other perks on the Lyft platform.
-
A.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
-
B.
Uber Green
Uber Green is an eco-focused ride option from Uber that connects riders with drivers using low-emission or electric vehicles.
-
C.
Uber Black
Uber Black is Uber’s premium ride service offering high-end vehicles and professional drivers for a more luxurious travel experience.
-
D.
Uber Pro
Uber Pro is a rewards and loyalty program that provides benefits and incentives to Uber drivers based on their performance and activity.
-
E.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
- 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efbd6ed481909ec40f12b5b675c8 |
completed | Feb. 28, 2026, 1:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a457fed1548190b50d72886828b650 |
completed | March 1, 2026, 3:15 p.m. |
| NEDg | Description generation | batch_69a4589d77408190b2d1b49e38b4ca4e |
completed | March 1, 2026, 3:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a45908403081909980377fe1741c41 |
completed | March 1, 2026, 3:19 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.