Triple
T20634018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAT trucks |
E507029
|
entity |
| Predicate | associatedBrand |
P1500
|
FINISHED |
| Object | Datsun |
—
|
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: Datsun | Statement: [DAT trucks, associatedBrand, Datsun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Datsun Context triple: [DAT trucks, associatedBrand, Datsun]
-
A.
Datsun
chosen
Datsun is a historic Japanese automobile brand, revived as a budget-focused marque under the Renault–Nissan–Mitsubishi Alliance and known for its small, affordable cars.
-
B.
Datsun Cherry
The Datsun Cherry was a compact front-wheel-drive car produced by Nissan in the 1970s and early 1980s, known as one of the company’s early global small cars.
-
C.
Datsun 810
The Datsun 810 is a late-1970s to early-1980s mid-size sedan and wagon produced by Nissan, known for its inline-six engines and for evolving into the Nissan Maxima.
-
D.
Nissan
Nissan is a river in southwestern Sweden that flows through the province of Halland before reaching the Kattegat.
-
E.
Nissan
Nissan is a major Japanese automobile manufacturer known for producing a wide range of passenger cars, trucks, and electric vehicles sold globally.
- 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_69e0b4bd4a0081908d4e97a590a33fb2 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad0d808c81908a60abd02a22ed92 |
completed | April 20, 2026, 10:47 p.m. |
Created at: April 16, 2026, 11:42 a.m.