Triple
T1659262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Reinhard |
E35866
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object | Lublin |
E47827
|
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: Lublin | Statement: [Operation Reinhard, administrativeCenter, Lublin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lublin Context triple: [Operation Reinhard, administrativeCenter, Lublin]
-
A.
Lublin
chosen
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.
-
B.
Łódź
Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
-
C.
Radom
Radom is a city in central Poland known as an important regional industrial and cultural center.
-
D.
Kielce
Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
-
E.
Tarnów
Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance 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_69a88606aa808190aa0b421b4271f220 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6277b11481909a894391f4feadd0 |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b324aac4fc819082e9c864e8a01126 |
completed | March 12, 2026, 8:40 p.m. |
Created at: March 4, 2026, 7:29 p.m.