Triple
T16566102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare de Lorraine TGV |
E402462
|
entity |
| Predicate | hasIATAcode |
P2569
|
FINISHED |
| Object | XZI |
E1221909
|
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: XZI | Statement: [Gare de Lorraine TGV, hasIATAcode, XZI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: XZI Context triple: [Gare de Lorraine TGV, hasIATAcode, XZI]
-
A.
XZI
chosen
XZI is the railway station code used to identify Gare de Lorraine TGV, a high-speed train station in northeastern France.
-
B.
xz
xz is a modern lossless data compression format and toolset based on the LZMA algorithm, known for achieving high compression ratios and commonly used for software distribution and archival on Unix-like systems.
-
C.
XUZ
XUZ is the IATA airport code for Xuzhou Guanyin International Airport, a commercial airport serving the city of Xuzhou in Jiangsu Province, China.
-
D.
IXZ
IXZ is the IATA airport code for Veer Savarkar International Airport serving Port Blair in the Andaman and Nicobar Islands, India.
-
E.
ZI
ZI is the vehicle registration code used on license plates for vehicles registered in the district of Görlitz in Germany.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e35772255881909f737da89bcd06b8 |
completed | April 18, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0075925b18819084c6d476eceea5f5 |
completed | May 10, 2026, 12:09 p.m. |
Created at: April 10, 2026, 5:15 a.m.