Triple
T3180619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shlisselburg |
E66575
|
entity |
| Predicate | originalName |
P65
|
FINISHED |
| Object |
Oreshek
Oreshek is the historic Russian fortress on Lake Ladoga that later gave rise to the town of Shlisselburg.
|
E336938
|
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: Oreshek | Statement: [Shlisselburg, originalName, Oreshek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oreshek Context triple: [Shlisselburg, originalName, Oreshek]
-
A.
Khovrino
Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
-
B.
Kuzminki
Kuzminki is a Moscow Metro station on the Tagansko–Krasnopresnenskaya Line serving the Kuzminki District in southeastern Moscow.
-
C.
Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
-
D.
Kologriv
Kologriv is a small historic town in Kostroma Oblast, Russia, known for its traditional wooden architecture and location within a forested, sparsely populated region.
-
E.
Sironcha
Sironcha is a town in the Gadchiroli district of Maharashtra, India, situated near the confluence of the Pranhita and Godavari rivers.
- 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: Oreshek Triple: [Shlisselburg, originalName, Oreshek]
Generated description
Oreshek is the historic Russian fortress on Lake Ladoga that later gave rise to the town of Shlisselburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oreshek Target entity description: Oreshek is the historic Russian fortress on Lake Ladoga that later gave rise to the town of Shlisselburg.
-
A.
Khovrino
Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
-
B.
Kuzminki
Kuzminki is a Moscow Metro station on the Tagansko–Krasnopresnenskaya Line serving the Kuzminki District in southeastern Moscow.
-
C.
Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
-
D.
Kologriv
Kologriv is a small historic town in Kostroma Oblast, Russia, known for its traditional wooden architecture and location within a forested, sparsely populated region.
-
E.
Sironcha
Sironcha is a town in the Gadchiroli district of Maharashtra, India, situated near the confluence of the Pranhita and Godavari rivers.
- 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_69ad8586a34c8190944c63ec11a8de1a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6a1280c8190b59a2afd30312c02 |
completed | March 8, 2026, 4:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2620b62f48190905812d0c0e9f85c |
completed | March 12, 2026, 6:49 a.m. |
| NEDg | Description generation | batch_69b2638b3b2881909563356ea8a9611c |
completed | March 12, 2026, 6:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b264fb42e4819084c289235f33b654 |
completed | March 12, 2026, 7:02 a.m. |
Created at: March 8, 2026, 3:06 p.m.