Triple
T13555126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marly |
E323749
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Marly@en |
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@en | Statement: [Marly, hasNameInLanguage, Marly@en]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marly@en Context triple: [Marly, hasNameInLanguage, Marly@en]
-
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.
Marj
Marj is a Libyan city located near the Jabal al Akhdar (Green Mountain) region in northeastern Libya.
-
C.
MARLANT
MARLANT is the Royal Canadian Navy’s Atlantic fleet formation responsible for naval operations, readiness, and support on Canada’s East Coast.
-
D.
Marlo
Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
-
E.
Marli
Marli is a given name commonly used as a feminine first name in various cultures.
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaff3063c8190bd20149b3f7df352 |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75da95b7c8190af4fae155f01d3af |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:47 p.m.