Triple
T680016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prévessin-Moëns |
E13160
|
entity |
| Predicate | sharesBorderWith |
P224
|
FINISHED |
| Object |
Sergy
Sergy is a small commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
|
E89751
|
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: Sergy | Statement: [Prévessin-Moëns, sharesBorderWith, Sergy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sergy Context triple: [Prévessin-Moëns, sharesBorderWith, Sergy]
-
A.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
B.
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
C.
Yuri
Yuri is a common Russian given name, famously borne by Yuri Gagarin, the first human to journey into outer space.
-
D.
Boyega
Boyega is the surname of British actor and producer John Boyega, best known for his role as Finn in the Star Wars sequel trilogy.
-
E.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
- 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: Sergy Triple: [Prévessin-Moëns, sharesBorderWith, Sergy]
Generated description
Sergy is a small commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sergy Target entity description: Sergy is a small commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
-
A.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
B.
Vasilevsky
Vasilevsky is a Russian surname most prominently associated with Aleksandr Vasilevsky, a leading Soviet military commander and Marshal of the Soviet Union during World War II.
-
C.
Yuri
Yuri is a common Russian given name, famously borne by Yuri Gagarin, the first human to journey into outer space.
-
D.
Boyega
Boyega is the surname of British actor and producer John Boyega, best known for his role as Finn in the Star Wars sequel trilogy.
-
E.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a04f4efc819082767a7517fa760a |
completed | March 1, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a65e3575388190a674df54e086fe2f |
completed | March 3, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_69a65e9a20748190b499182db9fc8cbb |
completed | March 3, 2026, 4:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a65f13d140819086042d9b21f842f8 |
completed | March 3, 2026, 4:09 a.m. |
Created at: March 1, 2026, 7:36 p.m.