Triple
T933679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Permanent Court of Arbitration |
E20148
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | PCA |
E109395
|
NE 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: PCA | Statement: [Permanent Court of Arbitration, abbreviation, PCA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PCA Context triple: [Permanent Court of Arbitration, abbreviation, PCA]
-
A.
PCA
PCA (Principal Component Analysis) in scikit-learn is a dimensionality reduction technique that transforms high-dimensional data into a smaller set of uncorrelated components capturing the most variance.
-
B.
PCA
chosen
The PCA is an intergovernmental organization based in The Hague that facilitates arbitration and other forms of dispute resolution between states, state entities, intergovernmental organizations, and private parties.
-
C.
KMeans
KMeans is a popular unsupervised machine learning algorithm used for partitioning data into a specified number of clusters based on feature similarity.
-
D.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
E.
variational autoencoders
Variational autoencoders are a class of generative neural networks that learn probabilistic latent representations of data, enabling them to generate new, similar samples.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a493af3dc48190adb7263e6e445ea1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3627ccc8190a836515b2ea85ec5 |
completed | March 1, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a826dec198819084889ea69d53cd6c |
completed | March 4, 2026, 12:34 p.m. |
Created at: March 1, 2026, 7:40 p.m.