Triple
T9769141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dungun River |
E237074
|
entity |
| Predicate | hasMouthNear |
P350
|
FINISHED |
| Object |
Dungun
Dungun is a coastal town in the state of Terengganu, Malaysia, known historically for fishing and nearby iron ore mining activities.
|
E823109
|
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: Dungun | Statement: [Dungun River, hasMouthNear, Dungun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dungun Context triple: [Dungun River, hasMouthNear, Dungun]
-
A.
Kuala Klawang
Kuala Klawang is a small town in Negeri Sembilan, Malaysia, known as the main administrative and commercial center of the surrounding Jelebu area.
-
B.
Dengkil
Dengkil is a town in the Sepang District of Selangor, Malaysia, known for its proximity to Putrajaya and Kuala Lumpur International Airport.
-
C.
Ratekau
Ratekau is a municipality in the district of Ostholstein in Schleswig-Holstein, northern Germany, near the Baltic Sea coast.
-
D.
Teluk Intan
Teluk Intan is a historic riverside town in the Malaysian state of Perak, known for its iconic leaning clock tower and colonial-era architecture.
-
E.
Kulim
Kulim is a prominent town and industrial hub in the Malaysian state of Kedah, known for its high-tech manufacturing and proximity to Penang.
- 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: Dungun Triple: [Dungun River, hasMouthNear, Dungun]
Generated description
Dungun is a coastal town in the state of Terengganu, Malaysia, known historically for fishing and nearby iron ore mining activities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dungun Target entity description: Dungun is a coastal town in the state of Terengganu, Malaysia, known historically for fishing and nearby iron ore mining activities.
-
A.
Kuala Klawang
Kuala Klawang is a small town in Negeri Sembilan, Malaysia, known as the main administrative and commercial center of the surrounding Jelebu area.
-
B.
Dengkil
Dengkil is a town in the Sepang District of Selangor, Malaysia, known for its proximity to Putrajaya and Kuala Lumpur International Airport.
-
C.
Ratekau
Ratekau is a municipality in the district of Ostholstein in Schleswig-Holstein, northern Germany, near the Baltic Sea coast.
-
D.
Teluk Intan
Teluk Intan is a historic riverside town in the Malaysian state of Perak, known for its iconic leaning clock tower and colonial-era architecture.
-
E.
Kulim
Kulim is a prominent town and industrial hub in the Malaysian state of Kedah, known for its high-tech manufacturing and proximity to Penang.
- 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_69ca84d831b8819090322686b47887ce |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda0f0c64c81908f3435dd49c0218b |
completed | April 1, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc46f170819081ecc5e85a0514c3 |
completed | April 5, 2026, 2:43 a.m. |
| NEDg | Description generation | batch_69d1ccbddbb08190aa09475ab99e15d6 |
completed | April 5, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1cd152d1081909abd7e30249fb887 |
completed | April 5, 2026, 2:46 a.m. |
Created at: March 30, 2026, 8:26 p.m.