Triple
T7320482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balıkesir Province |
E168529
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Havran
Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
|
E659594
|
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: Havran | Statement: [Balıkesir Province, containsCity, Havran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Havran Context triple: [Balıkesir Province, containsCity, Havran]
-
A.
Hungary
Hungary is a landlocked Central European country known for its rich history, distinct language (Hungarian), and capital city Budapest, famed for its thermal baths and architecture.
-
B.
Bohemia
Bohemia is a historical region in the western part of the modern Czech Republic, long a cultural and political center of Central Europe.
-
C.
Styria
Styria is a federal state in southeastern Austria known for its capital Graz, diverse landscapes, and strong industrial and educational sectors.
-
D.
Ungar
Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
-
E.
Malacky
Malacky is a small town in western Slovakia known for its historical center and location near the capital, Bratislava.
- 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: Havran Triple: [Balıkesir Province, containsCity, Havran]
Generated description
Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Havran Target entity description: Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
-
A.
Hungary
Hungary is a landlocked Central European country known for its rich history, distinct language (Hungarian), and capital city Budapest, famed for its thermal baths and architecture.
-
B.
Bohemia
Bohemia is a historical region in the western part of the modern Czech Republic, long a cultural and political center of Central Europe.
-
C.
Styria
Styria is a federal state in southeastern Austria known for its capital Graz, diverse landscapes, and strong industrial and educational sectors.
-
D.
Ungar
Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
-
E.
Malacky
Malacky is a small town in western Slovakia known for its historical center and location near the capital, Bratislava.
- 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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef1a7a3c81909504eb711056f302 |
completed | March 27, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802ae19d88190a2f7997a6f3dfb1e |
completed | March 28, 2026, 4:32 p.m. |
| NEDg | Description generation | batch_69c8039e1aa0819090a4a336c2f85583 |
completed | March 28, 2026, 4:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c803fc447c8190b1d16b47c90f982b |
completed | March 28, 2026, 4:38 p.m. |
Created at: March 27, 2026, 3:02 p.m.