Triple
T16401301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Marshall Mathers LP |
E398311
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Stan |
E398319
|
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: Stan | Statement: [The Marshall Mathers LP, hasPart, Stan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stan Context triple: [The Marshall Mathers LP, hasPart, Stan]
-
A.
Stan
Stan is an Australian subscription video-on-demand streaming service offering films, TV series, and original content.
-
B.
Stan
chosen
"Stan" is a critically acclaimed song by Eminem that tells the dark, narrative-driven story of an obsessive fan through a series of letters.
-
C.
Stan
Stan is a fast-talking, over-the-top used-boat and later used-coffin salesman known for his loud jacket and relentless sales pitches in the Monkey Island adventure game series.
-
D.
Stan
Stan is a probabilistic programming language and platform widely used for Bayesian statistical modeling and inference, particularly via methods like Hamiltonian Monte Carlo.
-
E.
Steve
Steve is a character best known as the calculating antagonist and betrayer in the heist film "The Italian Job."
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327cfb2fc8190bbc2765247c4b4e4 |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c5e7b4881908245228730a65876 |
completed | May 10, 2026, 8:05 a.m. |
Created at: April 10, 2026, 5:09 a.m.