Triple
T16816402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Rojas |
E408761
|
entity |
| Predicate | coFounded |
P104
|
FINISHED |
| Object | Engadget |
E66252
|
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: Engadget | Statement: [Peter Rojas, coFounded, Engadget]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Engadget Context triple: [Peter Rojas, coFounded, Engadget]
-
A.
Engadget
chosen
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
B.
Gizmodo
Gizmodo is a technology and design-focused news and opinion website known for its coverage of gadgets, science, and digital culture.
-
C.
CNET
CNET is a United States Navy command responsible for overseeing education and training programs for naval personnel.
-
D.
Gadget
Gadget is the bumbling yet well-intentioned cyborg detective protagonist of the animated series "Inspector Gadget," known for his numerous built-in mechanical devices.
-
E.
Tekno
Tekno is a Nigerian singer, songwriter, and record producer known for his Afrobeat and Afropop hit songs and dance-oriented sound.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b2e1de908190aa3508770fb865cf |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b297778c81909a2545c359739151 |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.