Triple
T33176080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Paul Island |
E849177
|
entity |
| Predicate | distanceToAmsterdamIsland_km |
P206370
|
FINISHED |
| Object | about 85 |
—
|
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 85 | Statement: [Saint-Paul Island, distanceToAmsterdamIsland_km, about 85]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAmsterdamIsland_km Context triple: [Saint-Paul Island, distanceToAmsterdamIsland_km, about 85]
-
A.
distanceToPitcairnIsland_km
Indicates the distance, measured in kilometers, between a given location and Pitcairn Island.
-
B.
distanceToAmsterdam
Indicates the spatial distance between a given location and the city of Amsterdam.
-
C.
distanceToTristanDaCunha
Indicates the spatial distance between a given entity and Tristan da Cunha.
-
D.
distanceToRebunIsland_km
Indicates the distance, measured in kilometers, from a given location to Rebun Island.
-
E.
distanceFromJuanFernandezIslands_km
Indicates the distance, measured in kilometers, between an entity and the Juan Fernández Islands.
- 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_69f3495d06508190b0b7729982982cea |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:29 a.m.