Triple
T4381607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ralph Merkle |
E99141
|
entity |
| Predicate | hasEmployer |
P7
|
FINISHED |
| Object | Zyvex |
E99145
|
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: Zyvex | Statement: [Ralph Merkle, hasEmployer, Zyvex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zyvex Context triple: [Ralph Merkle, hasEmployer, Zyvex]
-
A.
Zyvex
chosen
Zyvex is a pioneering nanotechnology company known for its early work in molecular nanotechnology and advanced manufacturing.
-
B.
Bazelevs Company
Bazelevs Company is a Russian film production company founded by director Timur Bekmambetov, known for producing genre films and pioneering screenlife-style movies.
-
C.
Onex
Onex is a suburban municipality in western Switzerland located just outside the city of Geneva.
-
D.
Zepbound
Zepbound is a prescription weight-loss medication developed by Eli Lilly that uses the GLP-1/GIP agonist tirzepatide to help adults with obesity or overweight manage their weight.
-
E.
Zenta
Zenta is a historic town in northern Serbia, best known as the site of a decisive 1697 battle between the Habsburg Monarchy and the Ottoman Empire.
- 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_69b3454ea8f48190a49c2436624d6ef6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352613dd481909e008a8db239a108 |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e51ff9188190aa4581d451feaafd |
completed | March 14, 2026, 10:45 p.m. |
Created at: March 12, 2026, 11:18 p.m.