Triple
T6810316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enrekang language |
E156612
|
entity |
| Predicate | alternateName |
P39
|
FINISHED |
| Object |
Endekan
Endekan is an alternate name for the Enrekang language, an Austronesian language spoken in South Sulawesi, Indonesia.
|
E620514
|
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: Endekan | Statement: [Enrekang language, alternateName, Endekan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Endekan Context triple: [Enrekang language, alternateName, Endekan]
-
A.
Ende
Ende is a coastal town and regency capital on the Indonesian island of Flores, known as a regional hub and gateway to nearby natural attractions.
-
B.
Denjaka
Denjaka is an elite Indonesian naval special forces unit specializing in maritime counter-terrorism, sabotage, and underwater operations.
-
C.
Dahegam
Dahegam is a town in the Indian state of Gujarat known for its local commerce and role as a regional hub within the Gandhinagar area.
-
D.
Lapseki
Lapseki is a town and district in Çanakkale Province in northwestern Turkey, situated on the Asian shore of the Dardanelles Strait.
-
E.
Lanaken
Lanaken is a municipality in the Belgian province of Limburg, known for its proximity to Maastricht and its mix of residential areas, industry, and natural landscapes.
- 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: Endekan Triple: [Enrekang language, alternateName, Endekan]
Generated description
Endekan is an alternate name for the Enrekang language, an Austronesian language spoken in South Sulawesi, Indonesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Endekan Target entity description: Endekan is an alternate name for the Enrekang language, an Austronesian language spoken in South Sulawesi, Indonesia.
-
A.
Ende
Ende is a coastal town and regency capital on the Indonesian island of Flores, known as a regional hub and gateway to nearby natural attractions.
-
B.
Denjaka
Denjaka is an elite Indonesian naval special forces unit specializing in maritime counter-terrorism, sabotage, and underwater operations.
-
C.
Dahegam
Dahegam is a town in the Indian state of Gujarat known for its local commerce and role as a regional hub within the Gandhinagar area.
-
D.
Lapseki
Lapseki is a town and district in Çanakkale Province in northwestern Turkey, situated on the Asian shore of the Dardanelles Strait.
-
E.
Lanaken
Lanaken is a municipality in the Belgian province of Limburg, known for its proximity to Maastricht and its mix of residential areas, industry, and natural landscapes.
- 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.