Triple
T146741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ivana Trump |
E3346
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Zlín
Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
|
E23399
|
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: Zlín | Statement: [Ivana Trump, placeOfBirth, Zlín]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zlín Context triple: [Ivana Trump, placeOfBirth, Zlín]
-
A.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
B.
Hradec Králové
Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
-
C.
Bratislava
Bratislava is the capital and largest city of Slovakia, situated along the Danube River near the borders with Austria and Hungary.
-
D.
Prague
Prague is the historic capital city of the Czech Republic, renowned for its well-preserved medieval architecture, iconic Charles Bridge and Prague Castle, and vibrant cultural life.
-
E.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
- 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: Zlín Triple: [Ivana Trump, placeOfBirth, Zlín]
Generated description
Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zlín Target entity description: Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
-
A.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
B.
Hradec Králové
Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
-
C.
Bratislava
Bratislava is the capital and largest city of Slovakia, situated along the Danube River near the borders with Austria and Hungary.
-
D.
Prague
Prague is the historic capital city of the Czech Republic, renowned for its well-preserved medieval architecture, iconic Charles Bridge and Prague Castle, and vibrant cultural life.
-
E.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a257eba6188190a3cf99c91bf3038f |
completed | Feb. 28, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2fd00c6b88190b5fc1180632cdb25 |
completed | Feb. 28, 2026, 2:34 p.m. |
| NEDg | Description generation | batch_69a2fdbc1edc8190b0647c4cbdabd03b |
completed | Feb. 28, 2026, 2:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2fe3550d48190b965018ed4e577a9 |
completed | Feb. 28, 2026, 2:39 p.m. |
Created at: Feb. 28, 2026, 2:31 a.m.