Triple
T22948925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piaggio |
E569952
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object |
PIA
PIA is the stock ticker symbol for Piaggio & C. SpA, the Italian manufacturer best known for producing Vespa scooters and other two- and three-wheeled motor vehicles.
|
E1563419
|
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: PIA | Statement: [Piaggio, tickerSymbol, PIA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PIA Context triple: [Piaggio, tickerSymbol, PIA]
-
A.
PIA
PIA is the ICAO airline designator for Pakistan International Airlines, the national flag carrier of Pakistan.
-
B.
PIA
PIA is the IATA airport code for General Wayne A. Downing Peoria International Airport, a commercial airport serving the Peoria, Illinois area.
-
C.
PI
PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
-
D.
PI
PI is the global industry organization responsible for developing and promoting the PROFIBUS and PROFINET industrial communication standards.
-
E.
PI
PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
- 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: PIA Triple: [Piaggio, tickerSymbol, PIA]
Generated description
PIA is the stock ticker symbol for Piaggio & C. SpA, the Italian manufacturer best known for producing Vespa scooters and other two- and three-wheeled motor vehicles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PIA Target entity description: PIA is the stock ticker symbol for Piaggio & C. SpA, the Italian manufacturer best known for producing Vespa scooters and other two- and three-wheeled motor vehicles.
-
A.
PIA
PIA is the ICAO airline designator for Pakistan International Airlines, the national flag carrier of Pakistan.
-
B.
PIA
PIA is the IATA airport code for General Wayne A. Downing Peoria International Airport, a commercial airport serving the Peoria, Illinois area.
-
C.
PI
PI is the global industry organization responsible for developing and promoting the PROFIBUS and PROFINET industrial communication standards.
-
D.
PI
PI is the vehicle registration code used on license plates for the district of Pinneberg in the German state of Schleswig-Holstein.
-
E.
PI
PI is a leading independent research institute in Waterloo, Canada, dedicated to advancing fundamental theoretical physics and fostering scientific collaboration and education.
- 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_69e2459199d08190a8184ee2aa935842 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1819fbf8c8190ad80c93f1507aa73 |
completed | April 29, 2026, 3:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bca161ddc8190923967d024e894ba |
completed | May 19, 2026, 2:25 a.m. |
| NEDg | Description generation | batch_6a0bcaebed688190b2ea74c588922d98 |
completed | May 19, 2026, 2:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bcbec354081909dbeeb389e86d38c |
completed | May 19, 2026, 2:33 a.m. |
Created at: April 17, 2026, 3:46 p.m.