Triple
T11945293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glory Sound Prep |
E284280
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | JT |
E758487
|
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: JT | Statement: [Glory Sound Prep, hasPart, JT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: JT Context triple: [Glory Sound Prep, hasPart, JT]
-
A.
JT
JT is a 1977 studio album by American singer-songwriter James Taylor that marked his commercial resurgence with hits like "Handy Man" and "Your Smiling Face."
-
B.
JT
chosen
JT is a lightweight 3D visualization and data exchange file format commonly used in CAD and PLM workflows for efficient sharing of complex product models.
-
C.
JK
JK is the widely used nickname of Juscelino Kubitschek, the former president of Brazil best known for founding Brasília and promoting rapid national development.
-
D.
JR
JR is a French street artist and photographer renowned for his large-scale public art installations that transform urban spaces and address social and political issues worldwide.
-
E.
JR
JR is the station code assigned to J. Ruiz station in the Manila Metro Rail Transit system.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903456ec0819082b8b10755a6b732 |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f440ac6c68819094bd27058d13cd4d |
completed | May 1, 2026, 5:57 a.m. |
Created at: April 8, 2026, 9:45 p.m.