Triple
T14601909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen of Anshan |
E342724
|
entity |
| Predicate | country |
P26
|
FINISHED |
| Object | Anshan |
E93127
|
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: Anshan | Statement: [Queen of Anshan, country, Anshan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anshan Context triple: [Queen of Anshan, country, Anshan]
-
A.
Anshan
chosen
Anshan was an ancient city and region in southwestern Iran that served as an early center of Elamite and later Achaemenid Persian power.
-
B.
Anshan
Anshan is a major industrial city in northeastern China, historically known as one of the country’s leading steel-producing centers.
-
C.
Benxi
Benxi is an industrial and mining city in eastern Liaoning Province, China, known for its steel production and nearby scenic karst landscapes.
-
D.
Liaoyuan
Liaoyuan is a prefecture-level city in northeastern China known for its coal mining history and location in the central part of Jilin Province.
-
E.
Fushun
Fushun is an industrial city in northeastern China known historically for its coal mining and heavy industry.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43a1bb48190bf520cb961f15b5e |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5c9a6748190878efa4970ce2b04 |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 10, 2026, 1:25 a.m.