Triple
T13267070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stick-breaking construction for the Indian buffet process |
E315949
|
entity |
| Predicate | relatesTo |
P37
|
FINISHED |
| Object | Indian buffet process exchangeable distribution |
E315949
|
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: Indian buffet process exchangeable distribution | Statement: [Stick-breaking construction for the Indian buffet process, relatesTo, Indian buffet process exchangeable distribution]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Indian buffet process exchangeable distribution Context triple: [Stick-breaking construction for the Indian buffet process, relatesTo, Indian buffet process exchangeable distribution]
-
A.
Stick-breaking construction for the Indian buffet process
chosen
"Stick-breaking construction for the Indian buffet process" is a research paper by Yee-Whye Teh that introduces a stick-breaking representation for the Indian buffet process, providing a constructive and interpretable way to model infinite latent feature allocations in Bayesian nonparametrics.
-
B.
Dirichlet process models
Dirichlet process models are a class of Bayesian nonparametric models that allow flexible, potentially infinite mixture modeling without fixing the number of components in advance.
-
C.
Pitman–Yor process models
Pitman–Yor process models are Bayesian nonparametric models that generalize Dirichlet process models by allowing power-law behavior and heavier-tailed distributions over clusters.
-
D.
Bayesian nonparametrics
Bayesian nonparametrics is a branch of Bayesian statistics that uses flexible, potentially infinite-dimensional models to let data determine model complexity rather than fixing a finite set of parameters in advance.
-
E.
Pólya’s urn model
Pólya’s urn model is a classic probabilistic scheme in which drawing and then reinforcing the color of balls in an urn produces rich-get-richer dynamics and illustrates concepts like contagion, dependence, and random reinforcement.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9901e44bc8190966f87ae219d6bf4 |
completed | April 11, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716cd8c2c8190a28d901fde98dc26 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:25 p.m.