Triple
T15099814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sack of Tursko |
E360633
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object |
Tursko
Tursko is a historical settlement known primarily as the site of the medieval Sack of Tursko.
|
E1136887
|
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: Tursko | Statement: [Sack of Tursko, location, Tursko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tursko Context triple: [Sack of Tursko, location, Tursko]
-
A.
Croatia
Croatia is a southeastern European country on the Adriatic Sea, known for its historic coastal cities, thousands of islands, and status as a member of both the European Union and NATO.
-
B.
Havran
Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
-
C.
Slovenia
Slovenia is a Central European country known for its mountains, lakes, and historic cities, and is a member of both the European Union and the Eurozone.
-
D.
Tunie
Tunie is the surname of American actress and director Tamara Tunie, best known for her long-running role on "Law & Order: Special Victims Unit."
-
E.
Rumen
Rumen is a masculine given name commonly used in Bulgaria and other Slavic countries.
- 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: Tursko Triple: [Sack of Tursko, location, Tursko]
Generated description
Tursko is a historical settlement known primarily as the site of the medieval Sack of Tursko.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tursko Target entity description: Tursko is a historical settlement known primarily as the site of the medieval Sack of Tursko.
-
A.
Croatia
Croatia is a southeastern European country on the Adriatic Sea, known for its historic coastal cities, thousands of islands, and status as a member of both the European Union and NATO.
-
B.
Havran
Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
-
C.
Slovenia
Slovenia is a Central European country known for its mountains, lakes, and historic cities, and is a member of both the European Union and the Eurozone.
-
D.
Tunie
Tunie is the surname of American actress and director Tamara Tunie, best known for her long-running role on "Law & Order: Special Victims Unit."
-
E.
Rumen
Rumen is a masculine given name commonly used in Bulgaria and other Slavic countries.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0054f00388190a5123d9f4a869b96 |
completed | April 15, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feae230d148190a343ac92fb089902 |
completed | May 9, 2026, 3:46 a.m. |
| NEDg | Description generation | batch_69feb0551a508190802f4073fa5ae4b4 |
completed | May 9, 2026, 3:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feb11fa1b081909243679603194b0b |
completed | May 9, 2026, 3:59 a.m. |
Created at: April 10, 2026, 3:04 a.m.