Triple
T4590319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower West Side |
E103469
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object |
Pilsen
Pilsen is a historically Mexican-American neighborhood on Chicago’s Lower West Side known for its vibrant murals, cultural institutions, and lively arts and food scene.
|
E455067
|
NE FINISHED |
How this triple was built (4 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: Pilsen | Statement: [Lower West Side, hasNeighborhood, Pilsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pilsen Context triple: [Lower West Side, hasNeighborhood, Pilsen]
-
A.
Pilsen
Pilsen is a city in the Czech Republic best known as the birthplace of Pilsner beer, a pale lager style that became one of the world’s most popular.
-
B.
Orel
Orel is a male given name most famously associated with former Major League Baseball pitcher Orel Hershiser.
-
C.
Cleves
Cleves is a historic town in western Germany near the Dutch border, known for its medieval castle and role as a former ducal capital in the Lower Rhine region.
-
D.
Osek
Osek is a town in the Czech Republic historically associated with the family origins of writer Franz Kafka’s father, Hermann Kafka.
-
E.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Pilsen Triple: [Lower West Side, hasNeighborhood, Pilsen]
Generated description
Pilsen is a historically Mexican-American neighborhood on Chicago’s Lower West Side known for its vibrant murals, cultural institutions, and lively arts and food scene.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pilsen Target entity description: Pilsen is a historically Mexican-American neighborhood on Chicago’s Lower West Side known for its vibrant murals, cultural institutions, and lively arts and food scene.
-
A.
Pilsen
Pilsen is a city in the Czech Republic best known as the birthplace of Pilsner beer, a pale lager style that became one of the world’s most popular.
-
B.
Orel
Orel is a male given name most famously associated with former Major League Baseball pitcher Orel Hershiser.
-
C.
Cleves
Cleves is a historic town in western Germany near the Dutch border, known for its medieval castle and role as a former ducal capital in the Lower Rhine region.
-
D.
Osek
Osek is a town in the Czech Republic historically associated with the family origins of writer Franz Kafka’s father, Hermann Kafka.
-
E.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
- F. None of above. chosen
Provenance (5 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_69bd43dccaf08190aa89e9991a289719 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5923c0c88190952137d448d474cf |
completed | March 20, 2026, 2:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bde0c1a7848190ac1e17fba6325593 |
completed | March 21, 2026, 12:05 a.m. |
| NEDg | Description generation | batch_69bde3078588819099b16bfc11330815 |
completed | March 21, 2026, 12:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bde394bd0081908417cc0a926a16dd |
completed | March 21, 2026, 12:17 a.m. |
Created at: March 20, 2026, 1:11 p.m.