Triple
T9032406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Longleng |
E216402
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Longleng |
E216402
|
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: Longleng | Statement: [Longleng, hasName, Longleng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Longleng Context triple: [Longleng, hasName, Longleng]
-
A.
Longleng
chosen
Longleng is a town and administrative center in the northeastern Indian state of Nagaland, known as the headquarters of Longleng district.
-
B.
Langho
Langho is a village in Lancashire, England, situated within the rural borough of Ribble Valley.
-
C.
Yangluo
Yangluo is a town in Wuhan, Hubei Province, China, known as an industrial and port area along the Yangtze River.
-
D.
Longqing
Longqing was the era name of a brief but notable period of the Ming dynasty in China, associated with the reign of the Longqing Emperor in the 16th century.
-
E.
Hanxi Changlong
Hanxi Changlong is a metro station in Guangzhou, China, serving the popular Chimelong tourist and entertainment area.
- 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_69ca83d10b608190b2b2f8e0a7faaf14 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6aa0c89c81909792190f08fef8df |
completed | April 1, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdbc9c6e08190aa71d84316afc6d5 |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:08 p.m.