Triple
T5694859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Worcester |
E125512
|
entity |
| Predicate | distanceToCapeTown |
P23676
|
FINISHED |
| Object | approximately 120 km |
—
|
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 120 km | Statement: [Worcester, distanceToCapeTown, approximately 120 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCapeTown Context triple: [Worcester, distanceToCapeTown, approximately 120 km]
-
A.
distanceFromCapeTown
chosen
Indicates the measured distance between a given location and Cape Town.
-
B.
distanceToDurban_km
Indicates the physical distance, measured in kilometers, between a given location and the city of Durban.
-
C.
distanceToJohannesburg_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Johannesburg.
-
D.
distanceToSouthAfrica
Indicates the measured or calculated spatial distance between a given entity and the country of South Africa.
-
E.
distanceFromBulawayo
Indicates the measured spatial distance between a given location or entity and the city of Bulawayo.
- F. None of above.
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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:45 p.m.