Triple
T1999837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | easyGroup |
E43442
|
entity |
| Predicate | ownsBrand |
P1500
|
FINISHED |
| Object |
easyCar
easyCar is a car rental company within the easyGroup family of low-cost travel and service brands.
|
E223781
|
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: easyCar | Statement: [easyGroup, ownsBrand, easyCar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: easyCar Context triple: [easyGroup, ownsBrand, easyCar]
-
A.
Lancia Ypsilon
The Lancia Ypsilon is a small Italian city car known for its stylish design, upscale interior, and long-running popularity in the European supermini segment.
-
B.
Lotus Elise
The Lotus Elise is a lightweight, mid-engined British sports car renowned for its agile handling and minimalist, driver-focused design.
-
C.
Maxus
Maxus is a commercial vehicle brand known for producing vans, pickups, and light trucks, owned by the Chinese automotive giant SAIC Motor.
-
D.
Peugeot Expert
The Peugeot Expert is a light commercial van produced by the French automaker Peugeot, commonly used for cargo and passenger transport in European markets.
-
E.
Peugeot 106
The Peugeot 106 is a small city car produced by the French manufacturer Peugeot in the 1990s and early 2000s, known for its compact size, affordability, and popularity in European markets.
- 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: easyCar Triple: [easyGroup, ownsBrand, easyCar]
Generated description
easyCar is a car rental company within the easyGroup family of low-cost travel and service brands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: easyCar Target entity description: easyCar is a car rental company within the easyGroup family of low-cost travel and service brands.
-
A.
Lancia Ypsilon
The Lancia Ypsilon is a small Italian city car known for its stylish design, upscale interior, and long-running popularity in the European supermini segment.
-
B.
Lotus Elise
The Lotus Elise is a lightweight, mid-engined British sports car renowned for its agile handling and minimalist, driver-focused design.
-
C.
Maxus
Maxus is a commercial vehicle brand known for producing vans, pickups, and light trucks, owned by the Chinese automotive giant SAIC Motor.
-
D.
Peugeot Expert
The Peugeot Expert is a light commercial van produced by the French automaker Peugeot, commonly used for cargo and passenger transport in European markets.
-
E.
Peugeot 106
The Peugeot 106 is a small city car produced by the French manufacturer Peugeot in the 1990s and early 2000s, known for its compact size, affordability, and popularity in European markets.
- 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_69a88715dbbc8190b2299e29e955d997 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb87f78f0819098e787a5f3e062fd |
completed | March 7, 2026, 5:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae034122ec819096a72685b34c84b9 |
completed | March 8, 2026, 11:16 p.m. |
| NEDg | Description generation | batch_69ae03b5e294819093b20fdc95f653db |
completed | March 8, 2026, 11:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae0475dd448190939620c5400eea16 |
completed | March 8, 2026, 11:21 p.m. |
Created at: March 4, 2026, 7:37 p.m.