Triple
T37201749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bierstadt family |
E922055
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Charles Bierstadt
Charles Bierstadt was a 19th-century American photographer known for his stereoscopic landscape views, particularly of Niagara Falls.
|
E269434
|
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: Charles Bierstadt | Statement: [Bierstadt family, hasMember, Charles Bierstadt]
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: Charles Bierstadt Triple: [Bierstadt family, hasMember, Charles Bierstadt]
Generated description
Charles Bierstadt was a 19th-century American photographer known for his stereoscopic landscape views, particularly of Niagara Falls.
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_69f76ea4849481909b4a3073efb0114c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb36460fec81908b92cdeb81a5e918 |
completed | May 6, 2026, 12:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4076e23fc48190863a1609ba0d9542 |
completed | June 28, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_6a407793fcf881909f668943a27834ca |
completed | June 28, 2026, 1:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a40781d2d808190b2118b042356795c |
completed | June 28, 2026, 1:25 a.m. |
Created at: May 3, 2026, 4:15 p.m.