Triple
T1903798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VWAG |
E37751
|
entity |
| Predicate | relatedTicker |
P3440
|
FINISHED |
| Object | VWAGY |
E37751
|
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: VWAGY | Statement: [VWAG, relatedTicker, VWAGY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VWAGY Context triple: [VWAG, relatedTicker, VWAGY]
-
A.
VWAG
chosen
VWAG is the stock ticker symbol under which the multinational automotive manufacturer Volkswagen Group is publicly traded.
-
B.
WDG
WDG is the abbreviated name for Wizards District Gaming, the NBA 2K League esports team affiliated with the Washington Wizards.
-
C.
LGW
LGW is the three-letter IATA airport code for London Gatwick Airport, a major international airport serving the London metropolitan area in the United Kingdom.
-
D.
ALWEG
ALWEG was a German transportation company best known for pioneering the modern straddle-beam monorail system that inspired several iconic monorail installations worldwide.
-
E.
VLG
VLG is the ICAO airline designator used to identify Vueling, a Spanish low-cost carrier based in Barcelona.
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1909aec8190b3259c8f969ce81e |
completed | March 7, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeaf768888190885ffa1632537445 |
completed | March 8, 2026, 9:32 p.m. |
Created at: March 4, 2026, 7:35 p.m.