Triple
T1766860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leningrad Oblast |
E38781
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Kingisepp
Kingisepp is a town in northwestern Russia near the Estonian border, known for its industrial base and historical roots dating back to the 14th century.
|
E197494
|
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: Kingisepp | Statement: [Leningrad Oblast, containsCity, Kingisepp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kingisepp Context triple: [Leningrad Oblast, containsCity, Kingisepp]
-
A.
Muroran
Muroran is an industrial port city in southern Hokkaido, Japan, known for its steel industry and scenic coastal landscapes.
-
B.
Podolsk
Podolsk is a major industrial city and former center of machine-building located just south of Moscow in western Russia.
-
C.
Khimki
Khimki is a city in Moscow Oblast, Russia, forming part of the Moscow metropolitan area and known for its proximity to major transport hubs and industrial facilities.
-
D.
Herzliya
Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
-
E.
Rubtsovsk
Rubtsovsk is an industrial city in Altai Krai, Russia, known as the birthplace of Raisa Gorbacheva and for its role as a regional agricultural and machinery center.
- 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: Kingisepp Triple: [Leningrad Oblast, containsCity, Kingisepp]
Generated description
Kingisepp is a town in northwestern Russia near the Estonian border, known for its industrial base and historical roots dating back to the 14th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kingisepp Target entity description: Kingisepp is a town in northwestern Russia near the Estonian border, known for its industrial base and historical roots dating back to the 14th century.
-
A.
Muroran
Muroran is an industrial port city in southern Hokkaido, Japan, known for its steel industry and scenic coastal landscapes.
-
B.
Podolsk
Podolsk is a major industrial city and former center of machine-building located just south of Moscow in western Russia.
-
C.
Khimki
Khimki is a city in Moscow Oblast, Russia, forming part of the Moscow metropolitan area and known for its proximity to major transport hubs and industrial facilities.
-
D.
Herzliya
Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
-
E.
Rubtsovsk
Rubtsovsk is an industrial city in Altai Krai, Russia, known as the birthplace of Raisa Gorbacheva and for its role as a regional agricultural and machinery center.
- 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa646914048190bbe282a3d4768835 |
completed | March 6, 2026, 5:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0f37a28819086c35c9f7a07dea9 |
completed | March 8, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69ada4da6a988190847452139e1c210d |
completed | March 8, 2026, 4:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ada55d96b88190a4a5c6973d69592d |
completed | March 8, 2026, 4:35 p.m. |
Created at: March 4, 2026, 7:31 p.m.