Triple
T17091536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Hamgyong Province |
E414737
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Orang
Orang is a coastal town and county seat in North Hamgyong Province, North Korea, known for its nearby airfield and agricultural surroundings.
|
E1249730
|
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: Orang | Statement: [North Hamgyong Province, hasMajorCity, Orang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orang Context triple: [North Hamgyong Province, hasMajorCity, Orang]
-
A.
Oru
Oru is a town in Ijebu North Local Government Area of Ogun State in southwestern Nigeria.
-
B.
Oga
Oga is a coastal city in northern Japan known for the Oga Peninsula and its traditional Namahage folklore.
-
C.
Oren
Oren is a masculine given name of Hebrew origin, commonly interpreted to mean "pine tree" or "ash tree."
-
D.
Orang Kanaq
Orang Kanaq are one of the smallest and least numerous indigenous Orang Asli groups of Peninsular Malaysia, traditionally living as forest-dwelling hunter-gatherers and horticulturalists.
-
E.
Orka
Orka is a super-strong, whale-themed Marvel Comics villain and occasional antihero who has served on teams like the Heroes for Hire.
- 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: Orang Triple: [North Hamgyong Province, hasMajorCity, Orang]
Generated description
Orang is a coastal town and county seat in North Hamgyong Province, North Korea, known for its nearby airfield and agricultural surroundings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orang Target entity description: Orang is a coastal town and county seat in North Hamgyong Province, North Korea, known for its nearby airfield and agricultural surroundings.
-
A.
Oru
Oru is a town in Ijebu North Local Government Area of Ogun State in southwestern Nigeria.
-
B.
Oga
Oga is a coastal city in northern Japan known for the Oga Peninsula and its traditional Namahage folklore.
-
C.
Oren
Oren is a masculine given name of Hebrew origin, commonly interpreted to mean "pine tree" or "ash tree."
-
D.
Orang Kanaq
Orang Kanaq are one of the smallest and least numerous indigenous Orang Asli groups of Peninsular Malaysia, traditionally living as forest-dwelling hunter-gatherers and horticulturalists.
-
E.
Orka
Orka is a super-strong, whale-themed Marvel Comics villain and occasional antihero who has served on teams like the Heroes for Hire.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfa09b08190be4303dd0d174feb |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ee9fd108190b12e8624bb66caf2 |
completed | May 11, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_6a012fe2a1b081909483baef845cc2c1 |
completed | May 11, 2026, 1:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0130c2ad9881909d8a8b64ebb59aa6 |
completed | May 11, 2026, 1:28 a.m. |
Created at: April 10, 2026, 5:35 a.m.