Triple
T3461529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Changling |
E73035
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Tianshou Mountain |
E359242
|
NE FINISHED |
How this triple was built (2 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: Tianshou Mountain | Statement: [Changling, near, Tianshou Mountain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tianshou Mountain Context triple: [Changling, near, Tianshou Mountain]
-
A.
Tianshou Mountain
chosen
Tianshou Mountain is a notable mountain in China, recognized for its scenic landscapes and cultural significance.
-
B.
Wuling Mountain
Wuling Mountain is a prominent peak in northern China known as the highest summit of the Yan Mountains range.
-
C.
Qixing Mountain
Qixing Mountain is a prominent volcanic peak in northern Taiwan known for its scenic hiking trails and panoramic views over Taipei.
-
D.
Xiaowutai Mountain
Xiaowutai Mountain is the highest peak of the Taihang mountain range in northern China, known for its rugged terrain and alpine scenery.
-
E.
Yufeng Mountain
Yufeng Mountain is a scenic, historically significant mountain and popular tourist destination located near Liuzhou in China’s Guangxi region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbae745e081909007cd3a664c57f3 |
completed | March 8, 2026, 6:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373aaff688190b9f12a7042055a1c |
completed | March 13, 2026, 2:17 a.m. |
Created at: March 8, 2026, 3:17 p.m.