Triple
T22404298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teresa |
E553842
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Teréz
Teréz is a Hungarian given name, equivalent to Teresa, commonly used for women in Hungary and other Hungarian-speaking communities.
|
E1533643
|
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: Teréz | Statement: [Teresa, hasVariant, Teréz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teréz Context triple: [Teresa, hasVariant, Teréz]
-
A.
Terézia
Terézia is the given name of the Hungarian-born German writer and translator Terézia Mora, known for her award-winning novels and screenplays.
-
B.
Mária
Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
-
C.
Katalin
Katalin is a Hungarian given name most prominently associated with biochemist Katalin Karikó, a pioneer of mRNA technology used in COVID-19 vaccines.
-
D.
Josepha
Josepha is the given name of Maria Josepha Amalia of Saxony, a 19th-century Saxon princess who became Queen consort of Spain as the third wife of King Ferdinand VII.
-
E.
Henriette Pressburg
Henriette Pressburg was the mother of philosopher and economist Karl Marx, belonging to a middle-class Jewish family in Trier before his later conversion to Protestantism.
- 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: Teréz Triple: [Teresa, hasVariant, Teréz]
Generated description
Teréz is a Hungarian given name, equivalent to Teresa, commonly used for women in Hungary and other Hungarian-speaking communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Teréz Target entity description: Teréz is a Hungarian given name, equivalent to Teresa, commonly used for women in Hungary and other Hungarian-speaking communities.
-
A.
Terézia
Terézia is the given name of the Hungarian-born German writer and translator Terézia Mora, known for her award-winning novels and screenplays.
-
B.
Mária
Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
-
C.
Katalin
Katalin is a Hungarian given name most prominently associated with biochemist Katalin Karikó, a pioneer of mRNA technology used in COVID-19 vaccines.
-
D.
Josepha
Josepha is the given name of Maria Josepha Amalia of Saxony, a 19th-century Saxon princess who became Queen consort of Spain as the third wife of King Ferdinand VII.
-
E.
Henriette Pressburg
Henriette Pressburg was the mother of philosopher and economist Karl Marx, belonging to a middle-class Jewish family in Trier before his later conversion to Protestantism.
- 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_69e11e4da7048190b4387d422a9a0de5 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f158b6762c8190991fc14c5ca8e609 |
completed | April 29, 2026, 1:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ae9cfabe081908d6a001bcfe5372c |
completed | May 18, 2026, 10:28 a.m. |
| NEDg | Description generation | batch_6a0aea5eef848190893c8b7f6066092d |
completed | May 18, 2026, 10:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0aeaf55efc81909438a39cbfd5e723 |
completed | May 18, 2026, 10:33 a.m. |
Created at: April 16, 2026, 8:46 p.m.