Triple
T8197343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Şile |
E191465
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Ağva
Ağva is a small coastal town on Turkey’s Black Sea shore, known for its beaches, rivers, and natural scenery that make it a popular getaway from Istanbul.
|
E718505
|
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: Ağva | Statement: [Şile, locatedNear, Ağva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ağva Context triple: [Şile, locatedNear, Ağva]
-
A.
Ağca
Ağca is the surname of Mehmet Ali Ağca, the Turkish gunman known for his 1981 assassination attempt on Pope John Paul II.
-
B.
Zangezur
Zangezur is a mountainous historical region in the South Caucasus, largely corresponding to today’s Syunik Province in southern Armenia and parts of neighboring territories.
-
C.
Yozgat
Yozgat is a city in central Turkey known for its location on the Anatolian plateau and its traditional Anatolian culture and history.
-
D.
Aghul
Aghul is a Northeast Caucasian language spoken by the Aghul people in southern Dagestan, Russia.
-
E.
Gaziemir
Gaziemir is a district of İzmir, Turkey, known for its proximity to the city’s main international airport and its role as a growing residential and commercial hub.
- 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: Ağva Triple: [Şile, locatedNear, Ağva]
Generated description
Ağva is a small coastal town on Turkey’s Black Sea shore, known for its beaches, rivers, and natural scenery that make it a popular getaway from Istanbul.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ağva Target entity description: Ağva is a small coastal town on Turkey’s Black Sea shore, known for its beaches, rivers, and natural scenery that make it a popular getaway from Istanbul.
-
A.
Ağca
Ağca is the surname of Mehmet Ali Ağca, the Turkish gunman known for his 1981 assassination attempt on Pope John Paul II.
-
B.
Zangezur
Zangezur is a mountainous historical region in the South Caucasus, largely corresponding to today’s Syunik Province in southern Armenia and parts of neighboring territories.
-
C.
Yozgat
Yozgat is a city in central Turkey known for its location on the Anatolian plateau and its traditional Anatolian culture and history.
-
D.
Aghul
Aghul is a Northeast Caucasian language spoken by the Aghul people in southern Dagestan, Russia.
-
E.
Gaziemir
Gaziemir is a district of İzmir, Turkey, known for its proximity to the city’s main international airport and its role as a growing residential and commercial hub.
- 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_69ca82c6e9548190a4c5ca14516e4417 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb5c2341f881908be59c378896e5bc |
completed | March 31, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccedb3cd488190b87e134dd0426a6d |
completed | April 1, 2026, 10:04 a.m. |
| NEDg | Description generation | batch_69ccf1b706f08190993f4a75eac5f49c |
completed | April 1, 2026, 10:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd05ac594c819087d23a7318fd7704 |
completed | April 1, 2026, 11:46 a.m. |
Created at: March 30, 2026, 5:42 p.m.