Triple
T7861126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adrien-Marie Legendre |
E182500
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | method of least squares |
E29364
|
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: method of least squares | Statement: [Adrien-Marie Legendre, knownFor, method of least squares]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: method of least squares Context triple: [Adrien-Marie Legendre, knownFor, method of least squares]
-
A.
method of least squares
chosen
The method of least squares is a fundamental mathematical technique for estimating unknown parameters by minimizing the sum of squared differences between observed and predicted values, widely used in statistics, data fitting, and regression analysis.
-
B.
Linear Estimation
Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
-
C.
LSQ
LSQ is the three-letter station code for Leicester Square, a London Underground station in the West End.
-
D.
method of moments
The method of moments is a statistical technique for estimating distribution parameters by equating sample moments to theoretical moments.
-
E.
Gauss–Markov theorem
The Gauss–Markov theorem is a fundamental result in statistics stating that, under certain conditions, the ordinary least squares estimator is the best linear unbiased estimator (BLUE) of the coefficients in a linear regression model.
- 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb36bcb5cc8190a8a384ce0f020b9f |
completed | March 31, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b4138d081908a5ff16b79f0a0c8 |
completed | March 31, 2026, 5:27 a.m. |
Created at: March 30, 2026, 4:53 p.m.