Triple
T18734659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Üçyol station |
E458128
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
UYO
UYO is the station code for Üçyol, a metro station in İzmir, Turkey.
|
E1339582
|
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: UYO | Statement: [Üçyol station, hasStationCode, UYO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UYO Context triple: [Üçyol station, hasStationCode, UYO]
-
A.
Uyo
Uyo is the capital city of Akwa Ibom State in southeastern Nigeria, known as an administrative, commercial, and cultural center in the region.
-
B.
Kungur
Kungur is a historic Russian town in Perm Krai known for its ice cave, traditional trade heritage, and role as a regional cultural center.
-
C.
Icó
Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
-
D.
Ciudad Guayana
Ciudad Guayana is a major industrial city in eastern Venezuela, known for its steel and aluminum production and its strategic location at the confluence of the Orinoco and Caroní rivers.
-
E.
Cumbuco
Cumbuco is a coastal village in northeastern Brazil known for its sand dunes, lagoons, and strong winds that make it a popular destination for kitesurfing and other beach tourism.
- 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: UYO Triple: [Üçyol station, hasStationCode, UYO]
Generated description
UYO is the station code for Üçyol, a metro station in İzmir, Turkey.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UYO Target entity description: UYO is the station code for Üçyol, a metro station in İzmir, Turkey.
-
A.
Uyo
Uyo is the capital city of Akwa Ibom State in southeastern Nigeria, known as an administrative, commercial, and cultural center in the region.
-
B.
Kungur
Kungur is a historic Russian town in Perm Krai known for its ice cave, traditional trade heritage, and role as a regional cultural center.
-
C.
Icó
Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
-
D.
Ciudad Guayana
Ciudad Guayana is a major industrial city in eastern Venezuela, known for its steel and aluminum production and its strategic location at the confluence of the Orinoco and Caroní rivers.
-
E.
Cumbuco
Cumbuco is a coastal village in northeastern Brazil known for its sand dunes, lagoons, and strong winds that make it a popular destination for kitesurfing and other beach tourism.
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d7a06788190a8e09c657aaeb8e5 |
completed | April 20, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05325fae0c8190a7f1a726ad86e3b6 |
completed | May 14, 2026, 2:24 a.m. |
| NEDg | Description generation | batch_6a0532ce7c388190a8d6b2d83ec52354 |
completed | May 14, 2026, 2:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a053363f09c8190a7927345f1d027b3 |
completed | May 14, 2026, 2:28 a.m. |
Created at: April 10, 2026, 11:51 a.m.