Triple
T10823729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabal Katrinah |
E255439
|
entity |
| Predicate | distanceFromMountSinai |
P95930
|
FINISHED |
| Object | a few kilometres |
—
|
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: a few kilometres | Statement: [Gabal Katrinah, distanceFromMountSinai, a few kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMountSinai Context triple: [Gabal Katrinah, distanceFromMountSinai, a few kilometres]
-
A.
distance to Midtown Manhattan (miles)
Indicates the physical separation between a location and Midtown Manhattan, measured in miles.
-
B.
distanceToShanghai
Indicates the measured or specified distance between a given entity’s location and the city of Shanghai.
-
C.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
-
D.
distanceFromGrandCentral
Indicates the spatial distance between a given entity and Grand Central.
-
E.
distanceFromBoston
Indicates the spatial distance between a given entity’s location and the city of Boston.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734cf7918819094d36ea208c80d12 |
completed | April 9, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69d70d1bf3648190b36fa96ea018e0dc |
completed | April 9, 2026, 2:21 a.m. |
| PDg | Predicate description generation | batch_69d7101c96708190808fef73199e8482 |
completed | April 9, 2026, 2:34 a.m. |
Created at: April 8, 2026, 9:19 p.m.