Triple
T10235374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mendel University in Brno |
E243448
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
MENDELU
MENDELU is a public university in Brno, Czech Republic, known for its focus on agriculture, forestry, and related life sciences.
|
E851530
|
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: MENDELU | Statement: [Mendel University in Brno, hasAbbreviation, MENDELU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MENDELU Context triple: [Mendel University in Brno, hasAbbreviation, MENDELU]
-
A.
MUNAL
MUNAL is Mexico City’s National Museum of Art, renowned for its extensive collection of Mexican art from the 16th to the 20th century housed in a historic neoclassical building.
-
B.
Menendo
Menendo is a medieval Iberian given name of likely Germanic origin that served as the root for the patronymic surname Menéndez.
-
C.
Mengen
Mengen is a small town in the district of Sigmaringen in Baden-Württemberg, Germany, known for its historic center and location near the upper Danube.
-
D.
Manda
Manda is a lesser-known Dravidian language spoken by tribal communities in parts of eastern India, particularly in Odisha.
-
E.
Menen
Menen is a town in the Belgian province of West Flanders, located near the border with France.
- 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: MENDELU Triple: [Mendel University in Brno, hasAbbreviation, MENDELU]
Generated description
MENDELU is a public university in Brno, Czech Republic, known for its focus on agriculture, forestry, and related life sciences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MENDELU Target entity description: MENDELU is a public university in Brno, Czech Republic, known for its focus on agriculture, forestry, and related life sciences.
-
A.
MUNAL
MUNAL is Mexico City’s National Museum of Art, renowned for its extensive collection of Mexican art from the 16th to the 20th century housed in a historic neoclassical building.
-
B.
Menendo
Menendo is a medieval Iberian given name of likely Germanic origin that served as the root for the patronymic surname Menéndez.
-
C.
Mengen
Mengen is a small town in the district of Sigmaringen in Baden-Württemberg, Germany, known for its historic center and location near the upper Danube.
-
D.
Manda
Manda is a lesser-known Dravidian language spoken by tribal communities in parts of eastern India, particularly in Odisha.
-
E.
Menen
Menen is a town in the Belgian province of West Flanders, located near the border with France.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d20de15c8190a81f3e9803fdfcd1 |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f757b514819087f5d5f659c50c66 |
completed | April 9, 2026, 12:48 a.m. |
| NEDg | Description generation | batch_69d6fa2ea97081908395048218c0592b |
completed | April 9, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fcb5dc4c8190944a423a9d16a4b8 |
completed | April 9, 2026, 1:11 a.m. |
Created at: April 6, 2026, 11:21 a.m.