Triple
T4548941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bern tram network |
E110113
|
entity |
| Predicate | hasStop |
P17789
|
FINISHED |
| Object |
Köniz stop
Köniz stop is a public transport stop in the municipality of Köniz that serves passengers on the Bern tram network.
|
E452076
|
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: Köniz stop | Statement: [Bern tram network, hasStop, Köniz stop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Köniz stop Context triple: [Bern tram network, hasStop, Köniz stop]
-
A.
Koni
Koni is a diminutive form of the given name Konrad, typically used as an affectionate nickname.
-
B.
Konerko
Konerko is the surname of Paul Konerko, a former Major League Baseball first baseman best known for his long tenure and leadership with the Chicago White Sox.
-
C.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
-
D.
Kileler
Kileler is a municipality and village in the Thessaly region of central Greece, known historically for its agricultural character and the 1910 peasant uprising.
-
E.
Kinnim
Kinnim is a tractate of the Mishnah that deals with the laws of bird offerings and the complications arising from their possible mix-ups.
- 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: Köniz stop Triple: [Bern tram network, hasStop, Köniz stop]
Generated description
Köniz stop is a public transport stop in the municipality of Köniz that serves passengers on the Bern tram network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Köniz stop Target entity description: Köniz stop is a public transport stop in the municipality of Köniz that serves passengers on the Bern tram network.
-
A.
Koni
Koni is a diminutive form of the given name Konrad, typically used as an affectionate nickname.
-
B.
Konerko
Konerko is the surname of Paul Konerko, a former Major League Baseball first baseman best known for his long tenure and leadership with the Chicago White Sox.
-
C.
Kœnig
Kœnig is a French surname most notably associated with figures such as General Marie-Pierre Kœnig, a prominent military leader during World War II.
-
D.
Kileler
Kileler is a municipality and village in the Thessaly region of central Greece, known historically for its agricultural character and the 1910 peasant uprising.
-
E.
Kinnim
Kinnim is a tractate of the Mishnah that deals with the laws of bird offerings and the complications arising from their possible mix-ups.
- 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_69bd4412524c8190be5bcc9ddee91848 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57f3f8348190868e274ac4df87ce |
completed | March 20, 2026, 2:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdb945bd3881908e6c3f5f91b5f38e |
completed | March 20, 2026, 9:16 p.m. |
| NEDg | Description generation | batch_69bdbecf94d0819087519e44aab5a035 |
completed | March 20, 2026, 9:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdbf81f1208190946611fb6a1c20ba |
completed | March 20, 2026, 9:43 p.m. |
Created at: March 20, 2026, 1:05 p.m.