Triple
T15657251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PAD |
E376473
|
entity |
| Predicate | hasIataCode |
P2569
|
FINISHED |
| Object | QQP |
E376478
|
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: QQP | Statement: [PAD, hasIataCode, QQP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: QQP Context triple: [PAD, hasIataCode, QQP]
-
A.
QQP
chosen
QQP is the National Rail station code used to identify London Paddington railway station in the United Kingdom.
-
B.
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.
-
C.
QQ
QQ is a popular Chinese instant messaging and social platform developed by Tencent, offering chat, entertainment, and digital services to hundreds of millions of users.
-
D.
IQQ
IQQ is the IATA airport code for Diego Aracena International Airport, which serves the city of Iquique in northern Chile.
-
E.
QQM
QQM is the IATA station code for London Marylebone railway station, a central London terminus serving regional and commuter rail services.
- 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ef3cb8c8190a10815b675b341c1 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff679bb7f0819092a98c2981bc9267 |
completed | May 9, 2026, 4:58 p.m. |
Created at: April 10, 2026, 4:15 a.m.