Triple
T2766685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arsk Cemetery |
E61355
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Arsk
Arsk is a town in the Republic of Tatarstan, Russia, known as an administrative and historical center of the surrounding Arsky District.
|
E298093
|
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: Arsk | Statement: [Arsk Cemetery, namedAfter, Arsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arsk Context triple: [Arsk Cemetery, namedAfter, Arsk]
-
A.
Alushta
Alushta is a resort town on the southern coast of Crimea, known for its beaches, mild climate, and role as a popular Black Sea tourist destination.
-
B.
Sinop
Sinop is a historic port city on Turkey’s Black Sea coast, long valued for its strategic harbor and role in regional trade and defense.
-
C.
Arnavutköy
Arnavutköy is a district on the European side of Istanbul, Turkey, known for its rapidly developing urban areas and hosting the city’s main international airport.
-
D.
Balat
Balat is a historic neighborhood in Istanbul, Turkey, known for its colorful houses, steep cobbled streets, and rich Jewish and multicultural heritage.
-
E.
Port of Gemlik
The Port of Gemlik is a significant Turkish maritime hub on the Sea of Marmara, known especially for its role in container, automotive, and general cargo trade.
- 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: Arsk Triple: [Arsk Cemetery, namedAfter, Arsk]
Generated description
Arsk is a town in the Republic of Tatarstan, Russia, known as an administrative and historical center of the surrounding Arsky District.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arsk Target entity description: Arsk is a town in the Republic of Tatarstan, Russia, known as an administrative and historical center of the surrounding Arsky District.
-
A.
Alushta
Alushta is a resort town on the southern coast of Crimea, known for its beaches, mild climate, and role as a popular Black Sea tourist destination.
-
B.
Sinop
Sinop is a historic port city on Turkey’s Black Sea coast, long valued for its strategic harbor and role in regional trade and defense.
-
C.
Arnavutköy
Arnavutköy is a district on the European side of Istanbul, Turkey, known for its rapidly developing urban areas and hosting the city’s main international airport.
-
D.
Balat
Balat is a historic neighborhood in Istanbul, Turkey, known for its colorful houses, steep cobbled streets, and rich Jewish and multicultural heritage.
-
E.
Port of Gemlik
The Port of Gemlik is a significant Turkish maritime hub on the Sea of Marmara, known especially for its role in container, automotive, and general cargo trade.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd5762d08190a6286994a4e5dd92 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc048bc8481908a6f70e034167c2a |
completed | March 10, 2026, 6:55 a.m. |
| NEDg | Description generation | batch_69afc14239e48190ad20f660e88befcb |
completed | March 10, 2026, 6:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc202466c81908c300520173837dc |
completed | March 10, 2026, 7:02 a.m. |
Created at: March 6, 2026, 9:57 p.m.