Triple
T10611410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monywa |
E276016
|
entity |
| Predicate | distanceFromSagaing |
P94943
|
FINISHED |
| Object | approximately 105 kilometers northwest |
—
|
LITERAL 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: approximately 105 kilometers northwest | Statement: [Monywa, distanceFromSagaing, approximately 105 kilometers northwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSagaing Context triple: [Monywa, distanceFromSagaing, approximately 105 kilometers northwest]
-
A.
distanceFromSihanoukville
Indicates the measured distance between a given location and Sihanoukville.
-
B.
distanceFromKotaBharu
Indicates the spatial distance between an entity’s location and the city of Kota Bharu.
-
C.
distanceToSingapore
Indicates the physical distance between a given location or entity and Singapore.
-
D.
distanceToDaNang_km
Indicates the distance, measured in kilometers, between a given location and Da Nang.
-
E.
distanceFromCaoBangCity
Indicates the measured distance between a given location and Cao Bang City.
- F. None of above. chosen
Provenance (4 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df5a1450819082ad445712fb7868 |
completed | April 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69d6dd7a223c8190854409d76368f3e8 |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 7:33 p.m.