Triple
T7061601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziggy Marley |
E164229
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object | Nesta |
E164228
|
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: Nesta | Statement: [Ziggy Marley, middleName, Nesta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nesta Context triple: [Ziggy Marley, middleName, Nesta]
-
A.
Nesta
chosen
Nesta is the middle name of legendary Jamaican reggae musician and cultural icon Bob Marley.
-
B.
Neste
Neste is a Finnish oil refining and renewable fuels company known for producing sustainable diesel and aviation fuels.
-
C.
Nisseni
Nisseni are the inhabitants or natives of Caltanissetta, a city in central Sicily, Italy.
-
D.
Nesite
Nesite is the term commonly used by modern scholars for the Hittite language, an ancient Indo-European language once spoken in Anatolia.
-
E.
Nain
Nain is a renowned Iranian town famous for producing high-quality, finely knotted Persian carpets characterized by intricate designs and a typically light color palette.
- 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_69c688796c148190adb2f1596f595f22 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e45b7488819094d2dd337731dab9 |
completed | March 27, 2026, 8:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79c880dc08190813bd9bac580530a |
completed | March 28, 2026, 9:16 a.m. |
Created at: March 27, 2026, 2:38 p.m.