Triple
T10706780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marylebone station |
E252427
|
entity |
| Predicate | hasIataCode |
P2569
|
FINISHED |
| Object |
QQM
QQM is the IATA station code for London Marylebone railway station, a central London terminus serving regional and commuter rail services.
|
E880410
|
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: QQM | Statement: [Marylebone station, hasIataCode, QQM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: QQM Context triple: [Marylebone station, hasIataCode, QQM]
-
A.
QQQ
QQQ is a popular exchange-traded fund (ETF) that tracks the performance of the Nasdaq-100 Index, providing exposure to many of the largest non-financial companies listed on the Nasdaq stock market.
-
B.
QQP
QQP is the National Rail station code used to identify London Paddington railway station in the United Kingdom.
-
C.
QQW
QQW is the IATA airport code assigned to WAT, identifying a specific airport in the international air transport system.
-
D.
IQQ
IQQ is the IATA airport code for Diego Aracena International Airport, which serves the city of Iquique in northern Chile.
-
E.
MQMs
MQMs are Delta Air Lines’ status-qualifying miles that determine a SkyMiles member’s Medallion elite tier based on distance and fare class flown.
- 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: QQM Triple: [Marylebone station, hasIataCode, QQM]
Generated description
QQM is the IATA station code for London Marylebone railway station, a central London terminus serving regional and commuter rail services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: QQM Target entity description: QQM is the IATA station code for London Marylebone railway station, a central London terminus serving regional and commuter rail services.
-
A.
QQQ
QQQ is a popular exchange-traded fund (ETF) that tracks the performance of the Nasdaq-100 Index, providing exposure to many of the largest non-financial companies listed on the Nasdaq stock market.
-
B.
QQP
QQP is the National Rail station code used to identify London Paddington railway station in the United Kingdom.
-
C.
QQW
QQW is the IATA airport code assigned to WAT, identifying a specific airport in the international air transport system.
-
D.
IQQ
IQQ is the IATA airport code for Diego Aracena International Airport, which serves the city of Iquique in northern Chile.
-
E.
MQMs
MQMs are Delta Air Lines’ status-qualifying miles that determine a SkyMiles member’s Medallion elite tier based on distance and fare class flown.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fddfbed48190810bb3faee473fde |
completed | April 9, 2026, 1:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9990760b48190a05753974cdf556c |
completed | April 11, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69d99e8632688190b3746649a124ca09 |
completed | April 11, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69da625a1e8c8190b282e7a70bb7c876 |
completed | April 11, 2026, 3:01 p.m. |
Created at: April 8, 2026, 9:12 p.m.