Triple
T16305822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagqu |
E395906
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Sog County
Sog County is a county-level administrative division in northern Tibet, China, under the jurisdiction of Nagqu Prefecture-level city.
|
E1241747
|
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: Sog County | Statement: [Nagqu, contains, Sog County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sog County Context triple: [Nagqu, contains, Sog County]
-
A.
Sherman County
Sherman County is a rural county in central Nebraska known for its agricultural landscape and small communities.
-
B.
Sherman County
Sherman County is a sparsely populated rural county in the far northern part of the Texas Panhandle, known for its agriculture and wide-open High Plains landscape.
-
C.
Mills County
Mills County is a rural county in the southwestern part of the U.S. state of Iowa, known for its agricultural landscape and small communities.
-
D.
Roberts County
Roberts County is a sparsely populated county in the Texas Panhandle known for its ranching land and location along the Canadian River.
-
E.
Pulaski County
Pulaski County is a rural county in central Georgia known for its agricultural landscape and the city of Hawkinsville as its county seat.
- 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: Sog County Triple: [Nagqu, contains, Sog County]
Generated description
Sog County is a county-level administrative division in northern Tibet, China, under the jurisdiction of Nagqu Prefecture-level city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sog County Target entity description: Sog County is a county-level administrative division in northern Tibet, China, under the jurisdiction of Nagqu Prefecture-level city.
-
A.
Sherman County
Sherman County is a sparsely populated rural county in the far northern part of the Texas Panhandle, known for its agriculture and wide-open High Plains landscape.
-
B.
Sherman County
Sherman County is a rural county in central Nebraska known for its agricultural landscape and small communities.
-
C.
Mills County
Mills County is a rural county in the southwestern part of the U.S. state of Iowa, known for its agricultural landscape and small communities.
-
D.
Roberts County
Roberts County is a sparsely populated county in the Texas Panhandle known for its ranching land and location along the Canadian River.
-
E.
Pulaski County
Pulaski County is a rural county in central Georgia known for its agricultural landscape and the city of Hawkinsville as its county seat.
- 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_69d87f23bb088190a16fbb91a1957ea5 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e288d5619081909d0f8157cc487877 |
completed | April 17, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfb95b348190a006f699c01e85ce |
completed | May 10, 2026, 6:34 p.m. |
| NEDg | Description generation | batch_6a00d0e1650881909eacc90cf99787f4 |
completed | May 10, 2026, 6:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d1e024e88190bfcb50b42f37949b |
completed | May 10, 2026, 6:43 p.m. |
Created at: April 10, 2026, 5:06 a.m.