Triple
T18318484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakhee |
E438807
|
entity |
| Predicate | stoodAtStud |
P129597
|
FINISHED |
| Object |
Yushun Stallion Station
Yushun Stallion Station is a Japanese horse breeding and stud farm known for standing prominent Thoroughbred stallions.
|
E1317684
|
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: Yushun Stallion Station | Statement: [Sakhee, stoodAtStud, Yushun Stallion Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yushun Stallion Station Context triple: [Sakhee, stoodAtStud, Yushun Stallion Station]
-
A.
Qilian station
Qilian station is a metro station on the Taipei Metro system in New Taipei City, Taiwan.
-
B.
Norbulingka
Norbulingka is a historic summer palace and garden complex of the Dalai Lamas in Lhasa, Tibet, renowned for its Tibetan architecture and cultural significance.
-
C.
Issha Station
Issha Station is a railway station in Nagoya, Japan, serving passengers on the city’s Higashiyama Line.
-
D.
Hamar Station
Hamar Station is a railway station in the town of Hamar in Innlandet county, Norway, serving as a regional transport hub on the country’s rail network.
-
E.
Ringebu Station
Ringebu Station is a railway station in the village of Ringebu in Innlandet county, Norway, serving as a stop on the Dovre Line between Oslo and Trondheim.
- 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: Yushun Stallion Station Triple: [Sakhee, stoodAtStud, Yushun Stallion Station]
Generated description
Yushun Stallion Station is a Japanese horse breeding and stud farm known for standing prominent Thoroughbred stallions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yushun Stallion Station Target entity description: Yushun Stallion Station is a Japanese horse breeding and stud farm known for standing prominent Thoroughbred stallions.
-
A.
Qilian station
Qilian station is a metro station on the Taipei Metro system in New Taipei City, Taiwan.
-
B.
Norbulingka
Norbulingka is a historic summer palace and garden complex of the Dalai Lamas in Lhasa, Tibet, renowned for its Tibetan architecture and cultural significance.
-
C.
Issha Station
Issha Station is a railway station in Nagoya, Japan, serving passengers on the city’s Higashiyama Line.
-
D.
Hamar Station
Hamar Station is a railway station in the town of Hamar in Innlandet county, Norway, serving as a regional transport hub on the country’s rail network.
-
E.
Ringebu Station
Ringebu Station is a railway station in the village of Ringebu in Innlandet county, Norway, serving as a stop on the Dovre Line between Oslo and Trondheim.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50aa342a881909afcd995405027af |
completed | April 19, 2026, 5:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03c4c6094881909add4e2fe99a1bba |
completed | May 13, 2026, 12:24 a.m. |
| NEDg | Description generation | batch_6a03c52d0ba48190845d461df02c3aa3 |
completed | May 13, 2026, 12:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03c5dd07488190ae04c6b406ab29c9 |
completed | May 13, 2026, 12:29 a.m. |
Created at: April 10, 2026, 10:36 a.m.