Triple
T24771853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beltrami |
E619745
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Filippo Beltrami
Filippo Beltrami was an Italian army officer and partisan commander who became a symbol of the Resistance after being killed in combat against German and Fascist forces during World War II.
|
E2117158
|
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: Filippo Beltrami | Statement: [Beltrami, hasNotableBearer, Filippo Beltrami]
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: Filippo Beltrami Triple: [Beltrami, hasNotableBearer, Filippo Beltrami]
Generated description
Filippo Beltrami was an Italian army officer and partisan commander who became a symbol of the Resistance after being killed in combat against German and Fascist forces during World War II.
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_69e2fabd04488190a2d13c97be745a2d |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f410abf6588190ac997f02a1177c19 |
completed | May 1, 2026, 2:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3786b1cf408190a05ec092820cb539 |
completed | June 21, 2026, 6:37 a.m. |
| NEDg | Description generation | batch_6a378f7c14f881908b059b59ec6c892b |
completed | June 21, 2026, 7:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a37900b238c8190bda9ac2ff1af848e |
completed | June 21, 2026, 7:17 a.m. |
Created at: April 18, 2026, 4:31 a.m.