Triple
T10681328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tatra |
E251761
|
entity |
| Predicate | usedInCity |
P4810
|
FINISHED |
| Object | Krakow |
E12815
|
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: Krakow | Statement: [Tatra, usedInCity, Krakow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krakow Context triple: [Tatra, usedInCity, Krakow]
-
A.
Kraków
chosen
Kraków is one of Poland’s oldest and most historically significant cities, renowned for its well-preserved medieval core, royal heritage, and cultural institutions.
-
B.
Wrocław
Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
-
C.
Lublin
Lublin is a historic city in eastern Poland known as a major cultural, academic, and economic center and for its significant role in Polish political history.
-
D.
Warsaw
Warsaw is the capital and largest city of Poland, known for its resilient history, especially its near-total destruction in World War II and subsequent postwar reconstruction.
-
E.
Warsaw
Warsaw is a small city in northern Indiana known as the "Orthopedic Capital of the World" for its concentration of medical device manufacturers.
- 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fcc1b8d4819082fde22db39d8b24 |
completed | April 9, 2026, 1:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671788ec88190852df74698bc4518 |
completed | May 2, 2026, 9:49 p.m. |
Created at: April 8, 2026, 9:10 p.m.