Triple
T17587777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heckman correction |
E428368
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object | R |
—
|
NE NERFINISHED |
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: R | Statement: [Heckman correction, implementedIn, R]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: R Context triple: [Heckman correction, implementedIn, R]
-
A.
R
R is the Motion Picture Association of America (MPAA) film rating indicating that a movie is restricted to adult audiences, with those under 17 requiring accompanying parent or adult guardian.
-
B.
R
R is a New York City Subway service that runs along the Broadway Line in Manhattan and Queens, providing local transit through key commercial and residential areas.
-
C.
R
chosen
R is a widely used open-source programming language and environment focused on statistical computing, data analysis, and graphical visualization.
-
D.
R
R is a post-nominal letter used to denote a specific rank or class within the Danish Order of the Dannebrog.
-
E.
R
R is a letter of the modern Latin alphabet commonly used in numerous languages and writing systems worldwide.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e41bf08190963848f1597b6e9f |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.