Triple
T4651350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludwig III of Bavaria |
E102300
|
entity |
| Predicate | deathPlace |
P21
|
FINISHED |
| Object |
Sárvár
Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
|
E457876
|
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: Sárvár | Statement: [Ludwig III of Bavaria, deathPlace, Sárvár]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sárvár Context triple: [Ludwig III of Bavaria, deathPlace, Sárvár]
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
C.
Törökbálint
Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
-
D.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
E.
Kőszeg
Kőszeg is a historic Hungarian town near the Austrian border, renowned for its well-preserved medieval architecture and role in defending against Ottoman sieges.
- 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: Sárvár Triple: [Ludwig III of Bavaria, deathPlace, Sárvár]
Generated description
Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sárvár Target entity description: Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
C.
Törökbálint
Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
-
D.
Hódmezővásárhely
Hódmezővásárhely is a city in southeastern Hungary known for its agricultural traditions, pottery, and regional cultural heritage.
-
E.
Kőszeg
Kőszeg is a historic Hungarian town near the Austrian border, renowned for its well-preserved medieval architecture and role in defending against Ottoman sieges.
- 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_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd630343f88190954d19fcd18a5864 |
completed | March 20, 2026, 3:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfae7636881908244b86cba1c66b7 |
completed | March 21, 2026, 1:56 a.m. |
| NEDg | Description generation | batch_69bdfbc12acc8190b8116a6003abb3e3 |
completed | March 21, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfc44536c8190a71e52b0690a7570 |
completed | March 21, 2026, 2:02 a.m. |
Created at: March 20, 2026, 1:14 p.m.