Triple
T19704101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zeen web curation service |
E473166
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Zeen |
—
|
NE NERFINISHED |
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: Zeen | Statement: [Zeen web curation service, hasName, Zeen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zeen Context triple: [Zeen web curation service, hasName, Zeen]
-
A.
Zeen
chosen
Zeen was a web-based publishing tool that allowed users to easily create and share digital magazines and visual stories online.
-
B.
Zeeba
Zeeba is a Brazilian singer-songwriter and producer best known for his vocal collaborations on international electronic dance music hits.
-
C.
Zeehaen
Zeehaen was one of the two Dutch East India Company ships in Abel Tasman’s 1642–1643 voyage that led to the first known European contact with New Zealand and parts of Tasmania.
-
D.
Zeeb
Zeeb is a variant transliteration of the Hebrew name Ze'ev, which means "wolf" and is known from its appearance in the Hebrew Bible.
-
E.
Zegen
Zegen is a surname most notably associated with American actor Michael Zegen, known for his roles in television and film.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b8707081908fbf96c989d2d52d |
completed | April 20, 2026, 3:14 p.m. |
Created at: April 10, 2026, 1:46 p.m.