Triple
T37447584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graflex camera |
E930588
|
entity |
| Predicate | notableModel |
P1503
|
FINISHED |
| Object |
Super Graphic
Super Graphic is a later-generation large-format press camera produced by Graflex, known for its rugged metal body, improved movements, and suitability for professional field photography.
|
E2227727
|
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: Super Graphic | Statement: [Graflex camera, notableModel, Super Graphic]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Super Graphic Triple: [Graflex camera, notableModel, Super Graphic]
Generated description
Super Graphic is a later-generation large-format press camera produced by Graflex, known for its rugged metal body, improved movements, and suitability for professional field photography.
Provenance (5 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_69f76ec0b9488190b7a4fae632bd1d2f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb8e04d8a0819090297825f692054b |
completed | May 6, 2026, 6:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40825f77ac8190b6e38fc8216a406e |
completed | June 28, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_6a4084b430608190a8fd3d0245a555f4 |
completed | June 28, 2026, 2:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a408547e22881908c4f836d466222ab |
completed | June 28, 2026, 2:22 a.m. |
Created at: May 3, 2026, 4:17 p.m.