Triple
T1148136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makassar |
E23614
|
entity |
| Predicate | historicalName |
P65
|
FINISHED |
| Object |
Mangkasar
Mangkasar is the historical name for Makassar, a major port city and cultural center on the island of Sulawesi in Indonesia.
|
E131539
|
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: Mangkasar | Statement: [Makassar, historicalName, Mangkasar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mangkasar Context triple: [Makassar, historicalName, Mangkasar]
-
A.
Balikpapan
Balikpapan is a coastal city in East Kalimantan, Indonesia, known as a major oil and gas hub and one of the most developed urban centers on the island of Borneo.
-
B.
Naga City
Naga City is a major urban center in the Bicol Region of the Philippines, known as a cultural, religious, and educational hub.
-
C.
Manado
Manado is a major coastal city and the capital of North Sulawesi province in Indonesia, known as a gateway to the renowned Bunaken Marine Park.
-
D.
Palu
Palu is a coastal city on the Indonesian island of Sulawesi, known as the capital of Central Sulawesi province and a regional center for trade and administration.
-
E.
Labuan
Labuan is a coastal town in Banten, western Java, Indonesia, known as a gateway to nearby natural attractions and marine tourism areas.
- 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: Mangkasar Triple: [Makassar, historicalName, Mangkasar]
Generated description
Mangkasar is the historical name for Makassar, a major port city and cultural center on the island of Sulawesi in Indonesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mangkasar Target entity description: Mangkasar is the historical name for Makassar, a major port city and cultural center on the island of Sulawesi in Indonesia.
-
A.
Balikpapan
Balikpapan is a coastal city in East Kalimantan, Indonesia, known as a major oil and gas hub and one of the most developed urban centers on the island of Borneo.
-
B.
Naga City
Naga City is a major urban center in the Bicol Region of the Philippines, known as a cultural, religious, and educational hub.
-
C.
Manado
Manado is a major coastal city and the capital of North Sulawesi province in Indonesia, known as a gateway to the renowned Bunaken Marine Park.
-
D.
Palu
Palu is a coastal city on the Indonesian island of Sulawesi, known as the capital of Central Sulawesi province and a regional center for trade and administration.
-
E.
Labuan
Labuan is a coastal town in Banten, western Java, Indonesia, known as a gateway to nearby natural attractions and marine tourism areas.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc7041248190893e4c655dbd0604 |
completed | March 1, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5eb3ec3881908c8cb39b422fcc71 |
completed | March 7, 2026, 5:21 p.m. |
| NEDg | Description generation | batch_69ac5f248db081908596810839ee6160 |
completed | March 7, 2026, 5:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac5fb242488190bf99f63956aeda13 |
completed | March 7, 2026, 5:26 p.m. |
Created at: March 1, 2026, 7:44 p.m.