Triple
T6427272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berastagi |
E128090
|
entity |
| Predicate | distanceFromMedan |
P70821
|
FINISHED |
| Object | approximately 60 kilometers |
—
|
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 60 kilometers | Statement: [Berastagi, distanceFromMedan, approximately 60 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMedan Context triple: [Berastagi, distanceFromMedan, approximately 60 kilometers]
-
A.
distanceToJakarta_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Jakarta.
-
B.
distanceFromKualaLumpur
Indicates the spatial distance between a given location or entity and Kuala Lumpur.
-
C.
distanceFromNgurahRaiAirport
Indicates the measured distance between a given location and Ngurah Rai Airport.
-
D.
distanceFromDharmapuri
Indicates the measured distance between a given place or entity and the location of Dharmapuri.
-
E.
distanceFromManila
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
- 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_69c00838de888190af2eec0b80495efa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06920cef48190a884df8f12987a0d |
completed | March 22, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69c060f780b08190aa650b4d1fc51f21 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c062d290448190a2183158ef75d129 |
completed | March 22, 2026, 9:44 p.m. |
Created at: March 22, 2026, 4:44 p.m.