Triple
T8953708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaneš |
E213419
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Nesha |
E732785
|
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: Nesha | Statement: [Kaneš, alsoKnownAs, Nesha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nesha Context triple: [Kaneš, alsoKnownAs, Nesha]
-
A.
Nesha
chosen
Nesha is a given name that can refer to various people or entities, often used as a feminine personal name in different cultures.
-
B.
Keisha
Keisha is a feminine given name used in English-speaking communities, often associated with African-American culture.
-
C.
Nenê
Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
-
D.
Dameisha
Dameisha is a popular coastal area in Shenzhen, China, best known for its long sandy beach, seaside resorts, and recreational attractions.
-
E.
Nikkiya
Nikkiya is an American singer and rapper known for her collaborations in hip-hop and R&B, particularly with producer and artist K.E. on the Track (Keys).
- 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_69ca8399ad2081909f8fa41d4314c215 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc670f88d0819085d7308a5cf6c764 |
completed | April 1, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc210302c8190b1c062fcbcfeb0f6 |
completed | April 3, 2026, 1:35 p.m. |
Created at: March 30, 2026, 7 p.m.