Triple
T9367934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Available Light |
E225454
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Glisten |
E795018
|
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: Glisten | Statement: [Available Light, hasTrack, Glisten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glisten Context triple: [Available Light, hasTrack, Glisten]
-
A.
Glisten
chosen
"Glisten" is a track from the EP *Available Light*, likely showcasing the release’s atmospheric and reflective musical style.
-
B.
Glister
Glister is an oral care brand from Amway known for its toothpaste and related dental hygiene products.
-
C.
Luster
Luster is a minor but significant character in William Faulkner’s novel "The Sound and the Fury," serving as a young Black caretaker to Benjy Compson and reflecting the social and racial dynamics of the Compson household.
-
D.
Luster
Luster is a municipality in Vestland county, Norway, known for its dramatic fjord landscapes, glaciers, and historic stave churches.
-
E.
Sparkle
Sparkle is a Georgia-Pacific paper towel brand known for its affordable, everyday household cleaning products.
- 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_69ca842cbddc819099d71ecec48cf9e5 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd507f9ed8819092967b204faa4408 |
completed | April 1, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100d68d28819082f366f3b5493cfb |
completed | April 4, 2026, 12:15 p.m. |
Created at: March 30, 2026, 7:43 p.m.