Triple
T12082927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prague 6 |
E287726
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Lysolaje
Lysolaje is a residential district and former village in the northwestern part of Prague, known for its green spaces and proximity to natural reserves.
|
E968104
|
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: Lysolaje | Statement: [Prague 6, contains, Lysolaje]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lysolaje Context triple: [Prague 6, contains, Lysolaje]
-
A.
Lysol
Lysol is a well-known brand of household disinfectant and cleaning products widely used for sanitizing surfaces and killing germs.
-
B.
Lemonal
Lemonal is a small rural village in Belize known for its traditional Creole community and proximity to wetlands and wildlife.
-
C.
Looz
Looz is the historical name of the medieval County of Loon, a former principality in what is now eastern Belgium.
-
D.
Intoxibellas
Intoxibellas are the elite, magically enhanced supermodel heroines in Tyra Banks’s fantasy novel "Modelland," known for their extraordinary beauty and powers.
-
E.
Skoal
Skoal is a leading U.S. smokeless tobacco brand best known for its moist snuff products.
- 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: Lysolaje Triple: [Prague 6, contains, Lysolaje]
Generated description
Lysolaje is a residential district and former village in the northwestern part of Prague, known for its green spaces and proximity to natural reserves.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lysolaje Target entity description: Lysolaje is a residential district and former village in the northwestern part of Prague, known for its green spaces and proximity to natural reserves.
-
A.
Lysol
Lysol is a well-known brand of household disinfectant and cleaning products widely used for sanitizing surfaces and killing germs.
-
B.
Lemonal
Lemonal is a small rural village in Belize known for its traditional Creole community and proximity to wetlands and wildlife.
-
C.
Looz
Looz is the historical name of the medieval County of Loon, a former principality in what is now eastern Belgium.
-
D.
Intoxibellas
Intoxibellas are the elite, magically enhanced supermodel heroines in Tyra Banks’s fantasy novel "Modelland," known for their extraordinary beauty and powers.
-
E.
Skoal
Skoal is a leading U.S. smokeless tobacco brand best known for its moist snuff products.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915124e4c8190b0264c2a09e3c2f3 |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f66509208190b7206e78df41c2fe |
completed | May 2, 2026, 1:04 p.m. |
| NEDg | Description generation | batch_69f6022ecf38819080f0eb6a3a815c5b |
completed | May 2, 2026, 1:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f606560934819092ba4d4fa162b799 |
completed | May 2, 2026, 2:12 p.m. |
Created at: April 8, 2026, 9:48 p.m.