Triple
T21305305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mur River |
E525180
|
entity |
| Predicate | hasTributary |
P415
|
FINISHED |
| Object |
Pinka
Pinka is a river in Central Europe that flows through Austria and Hungary before joining the Mur River.
|
E1477237
|
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: Pinka | Statement: [Mur River, hasTributary, Pinka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pinka Context triple: [Mur River, hasTributary, Pinka]
-
A.
Katinka
Katinka is a 1915 operetta composed by Rudolf Friml, known for its light romantic plot and melodic score typical of early 20th-century musical theatre.
-
B.
Pippy
Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
-
C.
Pinky Rose
Pinky Rose is the shy, emotionally fragile young woman played by Sissy Spacek in Robert Altman’s 1977 psychological drama film "3 Women."
-
D.
Lány
Lány is a village and chateau area in the Czech Republic known as the site of the presidential summer residence and the place where the first Czechoslovak president Tomáš Garrigue Masaryk died.
-
E.
Bimba
Bimba is a central character in the French thriller film "The Wages of Fear," known as one of the desperate men who undertake a perilous mission to transport nitroglycerin.
- 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: Pinka Triple: [Mur River, hasTributary, Pinka]
Generated description
Pinka is a river in Central Europe that flows through Austria and Hungary before joining the Mur River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pinka Target entity description: Pinka is a river in Central Europe that flows through Austria and Hungary before joining the Mur River.
-
A.
Katinka
Katinka is a 1915 operetta composed by Rudolf Friml, known for its light romantic plot and melodic score typical of early 20th-century musical theatre.
-
B.
Pippy
Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
-
C.
Pinky Rose
Pinky Rose is the shy, emotionally fragile young woman played by Sissy Spacek in Robert Altman’s 1977 psychological drama film "3 Women."
-
D.
Lány
Lány is a village and chateau area in the Czech Republic known as the site of the presidential summer residence and the place where the first Czechoslovak president Tomáš Garrigue Masaryk died.
-
E.
Bimba
Bimba is a central character in the French thriller film "The Wages of Fear," known as one of the desperate men who undertake a perilous mission to transport nitroglycerin.
- 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_69e0b518b8948190ad69cf9a8784d397 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75aa3f90481909b212bdbf00c2bb0 |
completed | April 21, 2026, 11:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a099ed32130819081479d9845d74ac3 |
completed | May 17, 2026, 10:56 a.m. |
| NEDg | Description generation | batch_6a099f69f1e08190bf96197fcf42b897 |
completed | May 17, 2026, 10:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a099feeda3481909d8020763f32d430 |
completed | May 17, 2026, 11:01 a.m. |
Created at: April 16, 2026, 4:05 p.m.