Triple
T15929318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morang District |
E386281
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object |
Letang
Letang is a notable town in eastern Nepal’s Morang District, recognized as a local commercial and administrative center.
|
E1188585
|
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: Letang | Statement: [Morang District, hasMajorTown, Letang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Letang Context triple: [Morang District, hasMajorTown, Letang]
-
A.
Letang
Letang is the surname of Kris Letang, a professional ice hockey defenseman best known for his long career with the NHL’s Pittsburgh Penguins.
-
B.
Tilantongo
Tilantongo was a prominent pre-Columbian Mixtec city-state in present-day Oaxaca, Mexico, known as a political and cultural hub of the Mixtec civilization.
-
C.
Tatanga
Tatanga is a recurring alien villain in the Super Mario series, best known as the main antagonist of Super Mario Land and nemesis of Princess Daisy.
-
D.
Tangale
Tangale is a West Chadic language spoken primarily in Gombe State, northeastern Nigeria, by the Tangale people.
-
E.
Tangalan
Tangalan is a coastal municipality in the province of Aklan in the Philippines, known for its beaches, marine sanctuaries, and natural attractions.
- 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: Letang Triple: [Morang District, hasMajorTown, Letang]
Generated description
Letang is a notable town in eastern Nepal’s Morang District, recognized as a local commercial and administrative center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Letang Target entity description: Letang is a notable town in eastern Nepal’s Morang District, recognized as a local commercial and administrative center.
-
A.
Letang
Letang is the surname of Kris Letang, a professional ice hockey defenseman best known for his long career with the NHL’s Pittsburgh Penguins.
-
B.
Tilantongo
Tilantongo was a prominent pre-Columbian Mixtec city-state in present-day Oaxaca, Mexico, known as a political and cultural hub of the Mixtec civilization.
-
C.
Tatanga
Tatanga is a recurring alien villain in the Super Mario series, best known as the main antagonist of Super Mario Land and nemesis of Princess Daisy.
-
D.
Tangale
Tangale is a West Chadic language spoken primarily in Gombe State, northeastern Nigeria, by the Tangale people.
-
E.
Tangalan
Tangalan is a coastal municipality in the province of Aklan in the Philippines, known for its beaches, marine sanctuaries, and natural attractions.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156a39abc8190927818f6e185033a |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3bd67f48190aee4f892206d9326 |
completed | May 9, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69ffc621ef1c8190933238291d69d7e1 |
completed | May 9, 2026, 11:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffc6c0792c8190af7945983b7bb25a |
completed | May 9, 2026, 11:44 p.m. |
Created at: April 10, 2026, 4:52 a.m.