Triple
T14830156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Władysław III Spindleshanks |
E348676
|
entity |
| Predicate | governedTerritory |
P10006
|
FINISHED |
| Object | Kalisz |
—
|
NE NERFINISHED |
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: Kalisz | Statement: [Władysław III Spindleshanks, governedTerritory, Kalisz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalisz Context triple: [Władysław III Spindleshanks, governedTerritory, Kalisz]
-
A.
Kalisz
chosen
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
-
B.
Kielce
Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
-
C.
Wolsztyn
Wolsztyn is a town in western Poland known for its historic steam locomotive depot and annual steam engine parade.
-
D.
Tychy
Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
-
E.
Kluczbork
Kluczbork is a town in southern Poland known as a local administrative, cultural, and economic center in the Opole region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0748eec8190a39c94024c3a3e3e |
completed | April 14, 2026, 11:40 p.m. |
Created at: April 10, 2026, 1:51 a.m.