Triple
T11736812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samsun Province |
E279047
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Ladik
Ladik is a district and town in Turkey’s Black Sea region, known for its natural landscapes, lakes, and winter sports facilities.
|
E944356
|
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: Ladik | Statement: [Samsun Province, hasDistrict, Ladik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ladik Context triple: [Samsun Province, hasDistrict, Ladik]
-
A.
Makadara
Makadara is a residential and commercial neighborhood in Nairobi, Kenya, known for its dense population, vibrant local markets, and mix of low- to middle-income housing.
-
B.
Cijeruk
Cijeruk is a district in West Java, Indonesia, known for its hilly landscapes and proximity to the city of Bogor.
-
C.
Kastanitsa
Kastanitsa is a traditional stone-built village in the Peloponnese region of Greece, known for its well-preserved Tsakonian architecture and scenic mountain setting.
-
D.
Dahan
Dahan is a critically acclaimed Bengali film directed by Rituparno Ghosh that explores themes of gender, social hypocrisy, and moral courage.
-
E.
Ilisu
Ilisu is a historic mountain village in northwestern Azerbaijan known for its scenic landscapes, traditional architecture, and cultural heritage.
- 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: Ladik Triple: [Samsun Province, hasDistrict, Ladik]
Generated description
Ladik is a district and town in Turkey’s Black Sea region, known for its natural landscapes, lakes, and winter sports facilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ladik Target entity description: Ladik is a district and town in Turkey’s Black Sea region, known for its natural landscapes, lakes, and winter sports facilities.
-
A.
Makadara
Makadara is a residential and commercial neighborhood in Nairobi, Kenya, known for its dense population, vibrant local markets, and mix of low- to middle-income housing.
-
B.
Cijeruk
Cijeruk is a district in West Java, Indonesia, known for its hilly landscapes and proximity to the city of Bogor.
-
C.
Kastanitsa
Kastanitsa is a traditional stone-built village in the Peloponnese region of Greece, known for its well-preserved Tsakonian architecture and scenic mountain setting.
-
D.
Dahan
Dahan is a critically acclaimed Bengali film directed by Rituparno Ghosh that explores themes of gender, social hypocrisy, and moral courage.
-
E.
Ilisu
Ilisu is a historic mountain village in northwestern Azerbaijan known for its scenic landscapes, traditional architecture, and cultural heritage.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4edced48190b7a59dd45921828e |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f019b318188190bfb7effcf42974d2 |
completed | April 28, 2026, 2:21 a.m. |
| NEDg | Description generation | batch_69f0319271788190a105828ae7582668 |
completed | April 28, 2026, 4:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f05aa351888190a31092e6a9aee26b |
completed | April 28, 2026, 6:58 a.m. |
Created at: April 8, 2026, 9:41 p.m.