Triple
T6977521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Çanakkale Province |
E161750
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object |
Biga
Biga is a town and district in Turkey’s Çanakkale Province, known for its agricultural economy and location near the Marmara Sea.
|
E632736
|
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: Biga | Statement: [Çanakkale Province, hasMajorTown, Biga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biga Context triple: [Çanakkale Province, hasMajorTown, Biga]
-
A.
Bira
Bira is a coastal village in South Sulawesi, Indonesia, renowned as a traditional boatbuilding center where skilled craftsmen construct the iconic wooden pinisi sailing ships.
-
B.
Kasha
Kasha is a feminine given name used in various cultures, often as a diminutive or variant of names like Katarzyna or Kasia.
-
C.
Gamosa
Gamosa is a traditional Assamese handwoven cotton cloth, typically white with red borders and motifs, symbolizing respect, cultural identity, and social bonding in Assam.
-
D.
Soba
Soba was the medieval capital city of the Nubian kingdom of Alodia, a major Christian state in what is now Sudan.
-
E.
Kibibi
Kibibi is a lively and energetic host character in Disney's "Festival of the Lion King" stage show at Disney theme parks.
- 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: Biga Triple: [Çanakkale Province, hasMajorTown, Biga]
Generated description
Biga is a town and district in Turkey’s Çanakkale Province, known for its agricultural economy and location near the Marmara Sea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Biga Target entity description: Biga is a town and district in Turkey’s Çanakkale Province, known for its agricultural economy and location near the Marmara Sea.
-
A.
Bira
Bira is a coastal village in South Sulawesi, Indonesia, renowned as a traditional boatbuilding center where skilled craftsmen construct the iconic wooden pinisi sailing ships.
-
B.
Kasha
Kasha is a feminine given name used in various cultures, often as a diminutive or variant of names like Katarzyna or Kasia.
-
C.
Gamosa
Gamosa is a traditional Assamese handwoven cotton cloth, typically white with red borders and motifs, symbolizing respect, cultural identity, and social bonding in Assam.
-
D.
Soba
Soba was the medieval capital city of the Nubian kingdom of Alodia, a major Christian state in what is now Sudan.
-
E.
Kibibi
Kibibi is a lively and energetic host character in Disney's "Festival of the Lion King" stage show at Disney theme parks.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db68d25c8190a1776908619ad979 |
completed | March 27, 2026, 7:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c761af5bb48190af430e961efb34fb |
completed | March 28, 2026, 5:05 a.m. |
| NEDg | Description generation | batch_69c762cd5f388190980808321296abd3 |
completed | March 28, 2026, 5:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c763beb91881909e561aa5715a6976 |
completed | March 28, 2026, 5:14 a.m. |
Created at: March 27, 2026, 2:31 p.m.