Triple
T7407976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomini–Tolitoli languages |
E170927
|
entity |
| Predicate | hasMemberLanguage |
P7390
|
FINISHED |
| Object |
Balaesang
Balaesang is an Austronesian language of the Tomini–Tolitoli group spoken in Central Sulawesi, Indonesia.
|
E663995
|
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: Balaesang | Statement: [Tomini–Tolitoli languages, hasMemberLanguage, Balaesang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balaesang Context triple: [Tomini–Tolitoli languages, hasMemberLanguage, Balaesang]
-
A.
Sunam
Sunam is a town in the Sangrur district of Punjab, India, known as the birthplace of Indian revolutionary Udham Singh.
-
B.
Balgüe
Balgüe is a small rural village on Ometepe Island in Lake Nicaragua, known for its scenic setting near volcanic landscapes and eco-tourism lodges.
-
C.
Gulgong
Gulgong is a historic gold rush town in New South Wales, Australia, known for its well-preserved 19th-century streetscapes and heritage buildings.
-
D.
Lambayong
Lambayong is a municipality in the province of Sultan Kudarat in the Philippines, known for its predominantly agricultural economy and rural communities.
-
E.
Napareuli
Napareuli is a Georgian wine appellation in the Kakheti region, known for producing high-quality wines, particularly from the Saperavi grape.
- 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: Balaesang Triple: [Tomini–Tolitoli languages, hasMemberLanguage, Balaesang]
Generated description
Balaesang is an Austronesian language of the Tomini–Tolitoli group spoken in Central Sulawesi, Indonesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Balaesang Target entity description: Balaesang is an Austronesian language of the Tomini–Tolitoli group spoken in Central Sulawesi, Indonesia.
-
A.
Sunam
Sunam is a town in the Sangrur district of Punjab, India, known as the birthplace of Indian revolutionary Udham Singh.
-
B.
Balgüe
Balgüe is a small rural village on Ometepe Island in Lake Nicaragua, known for its scenic setting near volcanic landscapes and eco-tourism lodges.
-
C.
Gulgong
Gulgong is a historic gold rush town in New South Wales, Australia, known for its well-preserved 19th-century streetscapes and heritage buildings.
-
D.
Lambayong
Lambayong is a municipality in the province of Sultan Kudarat in the Philippines, known for its predominantly agricultural economy and rural communities.
-
E.
Napareuli
Napareuli is a Georgian wine appellation in the Kakheti region, known for producing high-quality wines, particularly from the Saperavi grape.
- 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_69c68a6010108190925e5284de022660 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f298f2388190afc944c9bc78749a |
completed | March 27, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81edbbe6481908904d1a1f7cfb20a |
completed | March 28, 2026, 6:32 p.m. |
| NEDg | Description generation | batch_69c8218838b48190a71dfa44a52ba30c |
completed | March 28, 2026, 6:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8230e2b3481909a6460a38be2a478 |
completed | March 28, 2026, 6:50 p.m. |
Created at: March 27, 2026, 3:10 p.m.