Triple
T12870585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thanbyuzayat |
E307838
|
entity |
| Predicate | distanceFromMawlamyine_km |
P107385
|
FINISHED |
| Object | about 60 |
—
|
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: about 60 | Statement: [Thanbyuzayat, distanceFromMawlamyine_km, about 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMawlamyine_km Context triple: [Thanbyuzayat, distanceFromMawlamyine_km, about 60]
-
A.
distanceFromMandalay
Indicates the measured distance between a given place or entity and the location of Mandalay.
-
B.
distanceToKohima_km
Indicates the physical distance, measured in kilometers, from a given entity or location to Kohima.
-
C.
distanceFromShillong
Indicates the spatial distance between a given entity or location and Shillong.
-
D.
distanceFromKathmandu
Indicates the measured spatial distance between a given location or entity and the city of Kathmandu.
-
E.
distanceFromGuwahati_km
Indicates the physical distance, measured in kilometers, between an entity and the location of Guwahati.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d97c7d0598819080cab0a2314bc106 |
completed | April 10, 2026, 10:41 p.m. |
Created at: April 9, 2026, 5:38 p.m.