Triple
T34659943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariental |
E890077
|
entity |
| Predicate | roadDistanceToKeetmanshoop_km |
P179583
|
FINISHED |
| Object | about 240 |
—
|
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 240 | Statement: [Mariental, roadDistanceToKeetmanshoop_km, about 240]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadDistanceToKeetmanshoop_km Context triple: [Mariental, roadDistanceToKeetmanshoop_km, about 240]
-
A.
roadDistanceToUtrecht_km
Indicates the distance in kilometers between an entity and Utrecht when traveling by road.
-
B.
distanceToBloemfontein
Indicates the spatial distance between a given entity’s location and the city of Bloemfontein.
-
C.
roadDistanceToLeek_km
Indicates the distance in kilometers between an entity and Leek when traveling via the road network.
-
D.
distanceToNelspruit
Indicates the spatial distance between a given entity’s location and the location of Nelspruit.
-
E.
distanceToManzini_km
Indicates the physical distance, measured in kilometers, between a given location and Manzini.
- 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_69f349d906bc8190b2efd9eff237d94b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f722cc6d1c8190b3ecaa6ab88ce2fa |
completed | May 3, 2026, 10:26 a.m. |
| PD | Predicate disambiguation | batch_69f72157af108190880317a62e634bb0 |
completed | May 3, 2026, 10:20 a.m. |
| PDg | Predicate description generation | batch_69f7221bc57c819085c1464a45e61b2f |
completed | May 3, 2026, 10:23 a.m. |
Created at: May 1, 2026, 2:04 a.m.