Triple
T10440072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3rd Infantry Regiment (United States) |
E246143
|
entity |
| Predicate | distinctiveFeature |
P7153
|
FINISHED |
| Object | conducts over 20 funerals per day at Arlington National Cemetery in peak periods |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: conducts over 20 funerals per day at Arlington National Cemetery in peak periods | Statement: [3rd Infantry Regiment (United States), distinctiveFeature, conducts over 20 funerals per day at Arlington National Cemetery in peak periods]
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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fb9df6fc8190830f405ef955d64b |
completed | April 7, 2026, 12:42 p.m. |
Created at: April 6, 2026, 12:15 p.m.