Triple
T3055441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dinka language |
E60468
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Padang Dinka
Padang Dinka is a major dialect of the Dinka language spoken primarily by the Padang subgroup of the Dinka people in South Sudan.
|
E323724
|
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: Padang Dinka | Statement: [Dinka language, hasDialect, Padang Dinka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Padang Dinka Context triple: [Dinka language, hasDialect, Padang Dinka]
-
A.
Pelabuhan Ratu
Pelabuhan Ratu is a coastal town and bay in West Java, Indonesia, known for its scenic beaches, strong surf, and local fishing culture.
-
B.
Keningau
Keningau is a major inland town and administrative district in the interior region of the Malaysian state of Sabah.
-
C.
Bukittinggi
Bukittinggi is a historic highland city in West Sumatra, Indonesia, renowned as a major hub of Minangkabau culture, history, and tourism.
-
D.
Bantia
Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
-
E.
Manggala
Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
- 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: Padang Dinka Triple: [Dinka language, hasDialect, Padang Dinka]
Generated description
Padang Dinka is a major dialect of the Dinka language spoken primarily by the Padang subgroup of the Dinka people in South Sudan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Padang Dinka Target entity description: Padang Dinka is a major dialect of the Dinka language spoken primarily by the Padang subgroup of the Dinka people in South Sudan.
-
A.
Pelabuhan Ratu
Pelabuhan Ratu is a coastal town and bay in West Java, Indonesia, known for its scenic beaches, strong surf, and local fishing culture.
-
B.
Keningau
Keningau is a major inland town and administrative district in the interior region of the Malaysian state of Sabah.
-
C.
Bukittinggi
Bukittinggi is a historic highland city in West Sumatra, Indonesia, renowned as a major hub of Minangkabau culture, history, and tourism.
-
D.
Bantia
Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
-
E.
Manggala
Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9bf6b9948190bc957bfd1579c471 |
completed | March 8, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef03425c8190a44486ab563c210f |
completed | March 11, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69b1f2d6e2008190adad44758e2d4766 |
completed | March 11, 2026, 10:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1f34005c88190817c40dc30f99f14 |
completed | March 11, 2026, 10:57 p.m. |
Created at: March 8, 2026, 3:02 p.m.