Triple
T18050569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | math (Python module) |
E431916
|
entity |
| Predicate | function |
P88
|
FINISHED |
| Object | math.dist |
—
|
NE NERFINISHED |
How this triple was built (3 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: math.dist | Statement: [math (Python module), function, math.dist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: math.dist Context triple: [math (Python module), function, math.dist]
-
A.
Measures of Distance
Measures of Distance is a 1988 video art piece by Mona Hatoum that layers intimate letters from her mother with fragmented images and sound to explore themes of exile, memory, and the complexities of mother–daughter relationships.
-
B.
Chebyshev distance (L-infinity metric)
Chebyshev distance (L-infinity metric) is a distance measure on a grid or in n-dimensional space defined as the maximum absolute difference along any coordinate axis between two points.
-
C.
distance covariance
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
D.
Media Distancia
Media Distancia is Spain’s network of medium-distance regional train services operated by Renfe, connecting major cities like Madrid with surrounding regions.
-
E.
Mahalanobis distance
Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: math.dist Target entity description: math.dist is a Python standard library function that computes the Euclidean distance between two points in n-dimensional space.
-
A.
Measures of Distance
Measures of Distance is a 1988 video art piece by Mona Hatoum that layers intimate letters from her mother with fragmented images and sound to explore themes of exile, memory, and the complexities of mother–daughter relationships.
-
B.
Chebyshev distance (L-infinity metric)
Chebyshev distance (L-infinity metric) is a distance measure on a grid or in n-dimensional space defined as the maximum absolute difference along any coordinate axis between two points.
-
C.
distance covariance
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
D.
Media Distancia
Media Distancia is Spain’s network of medium-distance regional train services operated by Renfe, connecting major cities like Madrid with surrounding regions.
-
E.
Mahalanobis distance
Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
- F. None of above. chosen
Provenance (2 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff57ea08190a30a87993f7d3299 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 10:25 a.m.