Triple
T5852455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qingdao |
E130067
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Laoshan Mountain |
E448663
|
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: Laoshan Mountain | Statement: [Qingdao, hasLandmark, Laoshan Mountain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laoshan Mountain Context triple: [Qingdao, hasLandmark, Laoshan Mountain]
-
A.
Laoshan Scenic Area
chosen
Laoshan Scenic Area is a famous coastal mountain tourist destination in Qingdao, China, known for its granite peaks, Taoist temples, and scenic views of the Yellow Sea.
-
B.
Xiaowutai Mountain
Xiaowutai Mountain is the highest peak of the Taihang mountain range in northern China, known for its rugged terrain and alpine scenery.
-
C.
Tianshou Mountain
Tianshou Mountain is a notable mountain in China, recognized for its scenic landscapes and cultural significance.
-
D.
Xiaohaituo Mountain
Xiaohaituo Mountain is a peak in Beijing’s Yanqing District that gained prominence as the mountainous backdrop and location for key venues of the 2022 Winter Olympics.
-
E.
Jiulongshan
Jiulongshan is a subway station in Beijing that serves as part of the city's extensive urban rail transit network.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0355038008190bf38980349b533e2 |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a1b8f8508190942ce1725884d254 |
completed | March 23, 2026, 2:13 a.m. |
Created at: March 22, 2026, 3:55 p.m.