Triple
T15239966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lemnos International Airport |
E364228
|
entity |
| Predicate | ICAO code |
P419
|
FINISHED |
| Object | LGLM |
E1100550
|
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: LGLM | Statement: [Lemnos International Airport, ICAO code, LGLM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LGLM Context triple: [Lemnos International Airport, ICAO code, LGLM]
-
A.
LGLM
chosen
LGLM is the ICAO airport code for Limnos International Airport, serving the island of Lemnos in Greece.
-
B.
GLM
GLM is the National Rail station code for Gillingham railway station in Kent, England.
-
C.
GLR
GLR is the IATA airport code for Gaylord Regional Airport, a public airport serving the Gaylord area in Michigan, United States.
-
D.
Bayesian logistic regression
Bayesian logistic regression is a probabilistic classification method that models binary outcomes using a logistic link function with prior distributions on the parameters, enabling full Bayesian inference and uncertainty quantification.
-
E.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007db9a148190aadea8d5f8b6b261 |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd41b7c48190917385c6c61370b2 |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 10, 2026, 3:13 a.m.