Triple
T15744358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esenboğa International Airport |
E381682
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Esenboğa
Esenboğa is a district and locality near Ankara, Turkey, best known for hosting Ankara’s main international airport.
|
E1176415
|
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: Esenboğa | Statement: [Esenboğa International Airport, locatedNear, Esenboğa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esenboğa Context triple: [Esenboğa International Airport, locatedNear, Esenboğa]
-
A.
Etimesgut
Etimesgut is a rapidly growing suburban district and municipality on the western side of Ankara, Turkey’s capital city.
-
B.
Çankaya
Çankaya is a central district of Ankara, Turkey, known for housing key government institutions, foreign embassies, and major national landmarks.
-
C.
Nişantaşı
Nişantaşı is an upscale neighborhood in Istanbul known for its luxury shopping streets, stylish cafes, and elegant residential buildings.
-
D.
Bağçasaray
Bağçasaray is the Crimean Tatar name for Bakhchisaray, a historic town in Crimea that once served as the capital of the Crimean Khanate.
-
E.
Mustafakemalpaşa
Mustafakemalpaşa is a town and district in northwestern Turkey known for its agricultural production and historical connection to Mustafa Kemal Atatürk.
- 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: Esenboğa Triple: [Esenboğa International Airport, locatedNear, Esenboğa]
Generated description
Esenboğa is a district and locality near Ankara, Turkey, best known for hosting Ankara’s main international airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Esenboğa Target entity description: Esenboğa is a district and locality near Ankara, Turkey, best known for hosting Ankara’s main international airport.
-
A.
Etimesgut
Etimesgut is a rapidly growing suburban district and municipality on the western side of Ankara, Turkey’s capital city.
-
B.
Çankaya
Çankaya is a central district of Ankara, Turkey, known for housing key government institutions, foreign embassies, and major national landmarks.
-
C.
Nişantaşı
Nişantaşı is an upscale neighborhood in Istanbul known for its luxury shopping streets, stylish cafes, and elegant residential buildings.
-
D.
Bağçasaray
Bağçasaray is the Crimean Tatar name for Bakhchisaray, a historic town in Crimea that once served as the capital of the Crimean Khanate.
-
E.
Mustafakemalpaşa
Mustafakemalpaşa is a town and district in northwestern Turkey known for its agricultural production and historical connection to Mustafa Kemal Atatürk.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502c0c3c8190b8e512df307039c1 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff9094b4008190bb5c65fa2bd0f0b5 |
completed | May 9, 2026, 7:52 p.m. |
| NEDg | Description generation | batch_69ff92c07f60819089c3faeea98e329c |
completed | May 9, 2026, 8:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9383ff1c81909e34995818a3c3f7 |
completed | May 9, 2026, 8:05 p.m. |
Created at: April 10, 2026, 4:46 a.m.