Triple
T6584811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marley |
E159193
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Marly |
E323749
|
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: Marly | Statement: [Marley, hasVariant, Marly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marly Context triple: [Marley, hasVariant, Marly]
-
A.
Marly
chosen
Marly is a French locality historically associated with royal architecture and landscape design, notably linked to the works of architect Jules Hardouin-Mansart.
-
B.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
C.
Marpissa
Marpissa is a traditional Cycladic village on the Greek island of Paros, known for its narrow alleys, whitewashed houses, and hilltop views.
-
D.
Iaso
Iaso is a minor Greek goddess associated with healing, recovery, and remedies, often linked to the cult of Asclepius.
-
E.
Mashobra
Mashobra is a serene hill town near Shimla in Himachal Pradesh, India, known for its lush forests, apple orchards, and tranquil mountain scenery.
- 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_69c688366ce8819083f8883983c0df92 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6aeacbde08190a2e4e82cd12bc43f |
completed | March 27, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbb00cc48190a49afdb82a267043 |
completed | March 27, 2026, 6:25 p.m. |
Created at: March 27, 2026, 1:54 p.m.