Triple
T12726439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turek |
E304119
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Łódź |
E15327
|
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: Łódź | Statement: [Turek, nearbyCity, Łódź]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Łódź Context triple: [Turek, nearbyCity, Łódź]
-
A.
Łódź
chosen
Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
-
B.
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.
-
C.
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.
-
D.
Wolsztyn
Wolsztyn is a town in western Poland known for its historic steam locomotive depot and annual steam engine parade.
-
E.
Kalisz
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
- 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96415ebe48190ae935bc3a9b00f65 |
completed | April 10, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd19164481909c2f35fbebf6150e |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 9, 2026, 5:25 p.m.