Triple
T17125659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auguste von Harrach |
E415586
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
von Harrach
Von Harrach is the name of an old Austrian noble family historically prominent in Central European aristocratic, political, and military circles.
|
E1251308
|
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: von Harrach | Statement: [Auguste von Harrach, familyName, von Harrach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: von Harrach Context triple: [Auguste von Harrach, familyName, von Harrach]
-
A.
Hácha
Hácha is a Czech surname most notably borne by Emil Hácha, the third President of Czechoslovakia who served during the early years of World War II.
-
B.
Havlíček
Havlíček is a Czech surname most famously associated with basketball Hall of Famer John Havlicek and several notable Czech cultural and public figures.
-
C.
Hájek
Hájek is a Czech surname borne by numerous notable figures in fields such as politics, science, and the arts.
-
D.
Ondrík
Ondrík is a Slovak diminutive form of the male given name Ondrej, used as an affectionate or familiar variant.
-
E.
Lumír Hanuš
Lumír Hanuš is a Czech analytical chemist and cannabis researcher best known for co-discovering the endocannabinoid anandamide.
- 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: von Harrach Triple: [Auguste von Harrach, familyName, von Harrach]
Generated description
Von Harrach is the name of an old Austrian noble family historically prominent in Central European aristocratic, political, and military circles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: von Harrach Target entity description: Von Harrach is the name of an old Austrian noble family historically prominent in Central European aristocratic, political, and military circles.
-
A.
Hácha
Hácha is a Czech surname most notably borne by Emil Hácha, the third President of Czechoslovakia who served during the early years of World War II.
-
B.
Havlíček
Havlíček is a Czech surname most famously associated with basketball Hall of Famer John Havlicek and several notable Czech cultural and public figures.
-
C.
Hájek
Hájek is a Czech surname borne by numerous notable figures in fields such as politics, science, and the arts.
-
D.
Ondrík
Ondrík is a Slovak diminutive form of the male given name Ondrej, used as an affectionate or familiar variant.
-
E.
Lumír Hanuš
Lumír Hanuš is a Czech analytical chemist and cannabis researcher best known for co-discovering the endocannabinoid anandamide.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f026ae188190b8c1e08529719878 |
completed | April 18, 2026, 8:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a12a7288190911c1be2667916c0 |
completed | May 11, 2026, 2:08 a.m. |
| NEDg | Description generation | batch_6a013a8e69388190b8d48d70a28e99bd |
completed | May 11, 2026, 2:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a013b6824888190853cf36548507e1b |
completed | May 11, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:36 a.m.