Triple
T11037140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darius Tanz |
E260914
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object | Tanz Industries |
E900720
|
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: Tanz Industries | Statement: [Darius Tanz, founded, Tanz Industries]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanz Industries Context triple: [Darius Tanz, founded, Tanz Industries]
-
A.
Tanz Industries
chosen
Tanz Industries is a powerful, high-tech aerospace and defense company in the TV series "Salvation," founded and led by billionaire scientist Darius Tanz.
-
B.
Shao Industries
Shao Industries is a company or corporate entity associated with and employing Liwen Shao.
-
C.
The Tannenbaum Company
The Tannenbaum Company is a television production company best known for producing the hit sitcom "Two and a Half Men."
-
D.
Litton Industries
Litton Industries was a major American conglomerate best known for its diversified operations in electronics, defense, and industrial manufacturing during the mid-20th century.
-
E.
Madison Industries
Madison Industries is a privately held industrial conglomerate based in Chicago, known for acquiring and growing a diverse portfolio of manufacturing and engineering businesses across sectors such as health, safety, and infrastructure.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797fd5fe081908af13835b18de7b8 |
completed | April 9, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c8372fac81908c68219b89ba45c1 |
completed | April 18, 2026, 6:06 p.m. |
Created at: April 8, 2026, 9:25 p.m.