Triple
T16460337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belp |
E399788
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Toffen
Toffen is a small Swiss municipality in the canton of Bern, located in the Gürbetal valley near the city of Bern.
|
E1214872
|
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: Toffen | Statement: [Belp, hasNeighboringMunicipality, Toffen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toffen Context triple: [Belp, hasNeighboringMunicipality, Toffen]
-
A.
Toffee
Toffee is a calculating, immortal lizard-like monster and master strategist who serves as the primary villain opposing Star Butterfly in the animated series "Star vs. the Forces of Evil."
-
B.
Taffy
Taffy is a character featured in the film "On the Line."
-
C.
Rosaroll
Rosaroll was an Italian Philhellene and military figure known for supporting the Greek War of Independence in the early 19th century.
-
D.
Twinkie
Twinkie is a hustling, street-smart high school student in The Fast and the Furious: Tokyo Drift who introduces the protagonist to Tokyo’s underground drift racing scene.
-
E.
Tortenstück
Tortenstück is the popular nickname for Frankfurt’s Museum für Moderne Kunst, known for its distinctive triangular, cake-slice-shaped architecture.
- 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: Toffen Triple: [Belp, hasNeighboringMunicipality, Toffen]
Generated description
Toffen is a small Swiss municipality in the canton of Bern, located in the Gürbetal valley near the city of Bern.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Toffen Target entity description: Toffen is a small Swiss municipality in the canton of Bern, located in the Gürbetal valley near the city of Bern.
-
A.
Toffee
Toffee is a calculating, immortal lizard-like monster and master strategist who serves as the primary villain opposing Star Butterfly in the animated series "Star vs. the Forces of Evil."
-
B.
Taffy
Taffy is a character featured in the film "On the Line."
-
C.
Rosaroll
Rosaroll was an Italian Philhellene and military figure known for supporting the Greek War of Independence in the early 19th century.
-
D.
Twinkie
Twinkie is a hustling, street-smart high school student in The Fast and the Furious: Tokyo Drift who introduces the protagonist to Tokyo’s underground drift racing scene.
-
E.
Tortenstück
Tortenstück is the popular nickname for Frankfurt’s Museum für Moderne Kunst, known for its distinctive triangular, cake-slice-shaped architecture.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d80e66c8190b2b3199efe9cfaa1 |
completed | April 18, 2026, 7:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f53aff081909a75de6672f15f0e |
completed | May 10, 2026, 9:26 a.m. |
| NEDg | Description generation | batch_6a0050a0f5b081908417c6062b1f50cc |
completed | May 10, 2026, 9:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00517b7b1c819098118fdbe03eb010 |
completed | May 10, 2026, 9:35 a.m. |
Created at: April 10, 2026, 5:10 a.m.