Triple
T6810944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yogyakarta International Airport |
E156630
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Temon
Temon is a district in the Kulon Progo Regency of Yogyakarta, Indonesia, known for hosting the region’s new Yogyakarta International Airport and serving as a growing transportation hub.
|
E620556
|
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: Temon | Statement: [Yogyakarta International Airport, locatedNear, Temon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Temon Context triple: [Yogyakarta International Airport, locatedNear, Temon]
-
A.
Ntem
Ntem is a town located in the South Region of Cameroon.
-
B.
Shiyali
Shiyali is a town in the Indian state of Tamil Nadu, historically known as the birthplace of pioneering librarian and mathematician S. R. Ranganathan.
-
C.
Shiyali
Shiyali is the given name of S. R. Ranganathan, the influential Indian mathematician and librarian known as the father of library science in India.
-
D.
Omaruru
Omaruru is a small historic town in central Namibia known for its colonial-era architecture, vineyards, and role as a local trading and farming center.
-
E.
Nimri
Nimri is a Spanish actress and singer best known for her roles in series like "Money Heist" and "Vis a Vis."
- 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: Temon Triple: [Yogyakarta International Airport, locatedNear, Temon]
Generated description
Temon is a district in the Kulon Progo Regency of Yogyakarta, Indonesia, known for hosting the region’s new Yogyakarta International Airport and serving as a growing transportation hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Temon Target entity description: Temon is a district in the Kulon Progo Regency of Yogyakarta, Indonesia, known for hosting the region’s new Yogyakarta International Airport and serving as a growing transportation hub.
-
A.
Ntem
Ntem is a town located in the South Region of Cameroon.
-
B.
Shiyali
Shiyali is a town in the Indian state of Tamil Nadu, historically known as the birthplace of pioneering librarian and mathematician S. R. Ranganathan.
-
C.
Shiyali
Shiyali is the given name of S. R. Ranganathan, the influential Indian mathematician and librarian known as the father of library science in India.
-
D.
Omaruru
Omaruru is a small historic town in central Namibia known for its colonial-era architecture, vineyards, and role as a local trading and farming center.
-
E.
Nimri
Nimri is a Spanish actress and singer best known for her roles in series like "Money Heist" and "Vis a Vis."
- 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_69c68828b26c819090fe9df7612bbc27 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30ded6481908fd64611607c610e |
completed | March 27, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71aa7dc3c81909ef422b5c51ae6be |
completed | March 28, 2026, 12:02 a.m. |
| NEDg | Description generation | batch_69c71e7cd6448190845888760c677eda |
completed | March 28, 2026, 12:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c71edd37048190bf4de088ad9f11de |
completed | March 28, 2026, 12:20 a.m. |
Created at: March 27, 2026, 2:16 p.m.