Triple
T1382703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss–Markov theorem |
E29373
|
entity |
| Predicate | topicIn |
P5303
|
FINISHED |
| Object | introductory econometrics courses |
—
|
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: introductory econometrics courses | Statement: [Gauss–Markov theorem, topicIn, introductory econometrics courses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topicIn Context triple: [Gauss–Markov theorem, topicIn, introductory econometrics courses]
-
A.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
B.
featuresTopic
Indicates that something (such as a work, event, or item) prominently includes, focuses on, or is organized around a particular topic.
-
C.
frequentlyDiscussedIn
chosen
Indicates that a topic, subject, or entity is often the focus of conversation, debate, or mention within a particular context or medium.
-
D.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
E.
themeFor
Indicates that something serves as the central subject, topic, or focus for another thing (such as an event, work, or activity).
- F. None of above.
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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3361bf08190b3f6bbf82e17685b |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.