Triple
T13230682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murten |
E315007
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Greng
Greng is a small municipality in the canton of Fribourg in western Switzerland, situated near the town of Murten and Lake Murten.
|
E1028984
|
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: Greng | Statement: [Murten, hasNeighboringMunicipality, Greng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greng Context triple: [Murten, hasNeighboringMunicipality, Greng]
-
A.
Grong
Grong is a rural municipality in Trøndelag county, central Norway, known for its forests, rivers, and role as a regional transport and service center in the Namdalen district.
-
B.
Kreng
Kreng is the stage name of Belgian composer Pepijn Caudron, known for his dark, atmospheric soundtracks and experimental electronic music.
-
C.
Griend
Griend is a small, uninhabited Dutch Wadden Sea island known as an important bird sanctuary and nature reserve.
-
D.
Breng
Breng is a Dutch public transport operator providing regional bus and train services in and around Arnhem and Nijmegen in the Netherlands.
-
E.
GROND
GROND is a multi-channel optical and near-infrared imaging instrument designed primarily for rapid follow-up observations of gamma-ray bursts and other transient astronomical events.
- 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: Greng Triple: [Murten, hasNeighboringMunicipality, Greng]
Generated description
Greng is a small municipality in the canton of Fribourg in western Switzerland, situated near the town of Murten and Lake Murten.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Greng Target entity description: Greng is a small municipality in the canton of Fribourg in western Switzerland, situated near the town of Murten and Lake Murten.
-
A.
Grong
Grong is a rural municipality in Trøndelag county, central Norway, known for its forests, rivers, and role as a regional transport and service center in the Namdalen district.
-
B.
Kreng
Kreng is the stage name of Belgian composer Pepijn Caudron, known for his dark, atmospheric soundtracks and experimental electronic music.
-
C.
Griend
Griend is a small, uninhabited Dutch Wadden Sea island known as an important bird sanctuary and nature reserve.
-
D.
Breng
Breng is a Dutch public transport operator providing regional bus and train services in and around Arnhem and Nijmegen in the Netherlands.
-
E.
GROND
GROND is a multi-channel optical and near-infrared imaging instrument designed primarily for rapid follow-up observations of gamma-ray bursts and other transient astronomical events.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d336ae08190bfc118cfbefddf84 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ff2c07488190ad07c544cca63a7d |
completed | May 3, 2026, 7:54 a.m. |
| NEDg | Description generation | batch_69f70408b2088190989c3b38a5d66495 |
completed | May 3, 2026, 8:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f70518acc0819089a987abfd42f928 |
completed | May 3, 2026, 8:19 a.m. |
Created at: April 9, 2026, 9:21 p.m.