Triple
T18929241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khulo cable car |
E463055
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object |
Tago
Tago is a small mountain village in southwestern Georgia known for being reached by the scenic Khulo cable car.
|
E1350192
|
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: Tago | Statement: [Khulo cable car, connects, Tago]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tago Context triple: [Khulo cable car, connects, Tago]
-
A.
Tago
Tago is a coastal municipality in the province of Surigao del Sur in the Philippines, known for its agricultural lands and riverine landscapes.
-
B.
Tagaeri
The Tagaeri are an uncontacted Indigenous group living in voluntary isolation in the Ecuadorian Amazon, known for resisting outside contact and encroachment on their territory.
-
C.
Tatengue
Tatengue is the popular nickname of Club Atlético Unión, a traditional football club from Santa Fe, Argentina.
-
D.
Tonoas
Tonoas is one of the main inhabited islands of Chuuk Lagoon in the Federated States of Micronesia, historically significant as a former Japanese administrative and military center in the Pacific.
-
E.
Tiba
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
- 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: Tago Triple: [Khulo cable car, connects, Tago]
Generated description
Tago is a small mountain village in southwestern Georgia known for being reached by the scenic Khulo cable car.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tago Target entity description: Tago is a small mountain village in southwestern Georgia known for being reached by the scenic Khulo cable car.
-
A.
Tago
Tago is a coastal municipality in the province of Surigao del Sur in the Philippines, known for its agricultural lands and riverine landscapes.
-
B.
Tagaeri
The Tagaeri are an uncontacted Indigenous group living in voluntary isolation in the Ecuadorian Amazon, known for resisting outside contact and encroachment on their territory.
-
C.
Tatengue
Tatengue is the popular nickname of Club Atlético Unión, a traditional football club from Santa Fe, Argentina.
-
D.
Tonoas
Tonoas is one of the main inhabited islands of Chuuk Lagoon in the Federated States of Micronesia, historically significant as a former Japanese administrative and military center in the Pacific.
-
E.
Tiba
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
- 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_69d8dcfdbbb881909964fa5a75bd0b48 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c9bea84081908fbe657fb4657c0b |
completed | April 20, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05912043c48190813e3dc5e432b68e |
completed | May 14, 2026, 9:08 a.m. |
| NEDg | Description generation | batch_6a059582457081909a7d13a5644b8b08 |
completed | May 14, 2026, 9:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05965f09108190b19d2efa6e69a688 |
completed | May 14, 2026, 9:31 a.m. |
Created at: April 10, 2026, 11:59 a.m.