Triple
T11365035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monique Stalens |
E269180
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Częstochowa |
E128038
|
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: Częstochowa | Statement: [Monique Stalens, birthPlace, Częstochowa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Częstochowa Context triple: [Monique Stalens, birthPlace, Częstochowa]
-
A.
Częstochowa
chosen
Częstochowa is a city in southern Poland best known as a major Catholic pilgrimage center, home to the Jasna Góra Monastery and the revered Black Madonna icon.
-
B.
Kielce
Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
-
C.
Kalisz
Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
-
D.
Tarnów
Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
-
E.
Lubawa
Lubawa is a historic town in northern Poland known for its medieval heritage and location within the Warmian-Masurian region.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea4589908190948a8225768e1eec |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd54f264d48190be636796d694ceb1 |
completed | May 8, 2026, 3:13 a.m. |
Created at: April 8, 2026, 9:33 p.m.