Triple
T20491366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jispa |
E502751
|
entity |
| Predicate | roadAccessFrom |
P22549
|
FINISHED |
| Object | Keylong |
—
|
NE NERFINISHED |
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: Keylong | Statement: [Jispa, roadAccessFrom, Keylong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keylong Context triple: [Jispa, roadAccessFrom, Keylong]
-
A.
Keylong
chosen
Keylong is a small town in the Indian state of Himachal Pradesh, known as the main urban center of the remote Lahaul region in the Himalayas.
-
B.
Kettenis
Kettenis is a village and municipal section of the city of Eupen in the German-speaking Community of eastern Belgium.
-
C.
Master Kau
Master Kau is the stern yet comically superstitious Taoist priest and vampire hunter who serves as the central protagonist in the Hong Kong horror-comedy film "Mr. Vampire."
-
D.
Knightro
Knightro is the costumed knight mascot representing the University of Central Florida at its athletic events and campus activities.
-
E.
Kabalo
Kabalo is a town in southeastern Democratic Republic of the Congo, known historically as a colonial-era river and rail transport hub.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b0373881909dd3e9387f82eab4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cba5b708190bef437acf6321b81 |
completed | April 20, 2026, 9:38 p.m. |
Created at: April 16, 2026, 11:35 a.m.