Triple
T15692705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werong |
E380372
|
entity |
| Predicate | hasNameOrigin |
P3325
|
FINISHED |
| Object |
Mount Werong
Mount Werong is a locality and mountainous area in New South Wales, Australia, known for its rural landscape and proximity to the Great Dividing Range.
|
E1174374
|
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: Mount Werong | Statement: [Werong, hasNameOrigin, Mount Werong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Werong Context triple: [Werong, hasNameOrigin, Mount Werong]
-
A.
Mount Werong
Mount Werong is a prominent peak in New South Wales, Australia, known as the loftiest summit within the Blue Mountains region.
-
B.
Mount Heha
Mount Heha is the tallest mountain in Burundi, located in the Burundi Highlands near the city of Bujumbura.
-
C.
Mount Pangasun
Mount Pangasun is the tallest volcanic peak in the remote Babuyan Islands of the northern Philippines.
-
D.
Mount Vitsi
Mount Vitsi is a mountain in northern Greece near the border with Albania, known for its strategic role in the Greek Civil War and its forested slopes that now attract hikers and nature enthusiasts.
-
E.
Mount Gamalama
Mount Gamalama is an active stratovolcano that dominates Ternate Island in Indonesia’s Maluku Islands and is known for its frequent eruptions and conical peak.
- 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: Mount Werong Triple: [Werong, hasNameOrigin, Mount Werong]
Generated description
Mount Werong is a locality and mountainous area in New South Wales, Australia, known for its rural landscape and proximity to the Great Dividing Range.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mount Werong Target entity description: Mount Werong is a locality and mountainous area in New South Wales, Australia, known for its rural landscape and proximity to the Great Dividing Range.
-
A.
Mount Werong
Mount Werong is a prominent peak in New South Wales, Australia, known as the loftiest summit within the Blue Mountains region.
-
B.
Mount Heha
Mount Heha is the tallest mountain in Burundi, located in the Burundi Highlands near the city of Bujumbura.
-
C.
Mount Pangasun
Mount Pangasun is the tallest volcanic peak in the remote Babuyan Islands of the northern Philippines.
-
D.
Mount Vitsi
Mount Vitsi is a mountain in northern Greece near the border with Albania, known for its strategic role in the Greek Civil War and its forested slopes that now attract hikers and nature enthusiasts.
-
E.
Mount Gamalama
Mount Gamalama is an active stratovolcano that dominates Ternate Island in Indonesia’s Maluku Islands and is known for its frequent eruptions and conical peak.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f4f5a888190bd3681bcb9bbc02f |
completed | April 16, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82ee580c819082ad53db6da91f66 |
completed | May 9, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_69ff83b7a534819090e24491579376c3 |
completed | May 9, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff844fa00c8190a47eb46394db097b |
completed | May 9, 2026, 7 p.m. |
Created at: April 10, 2026, 4:44 a.m.