Triple
T38568527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 4th Battalion, 3rd Aviation Regiment |
E928296
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object |
3rd Aviation Regiment
The 3rd Aviation Regiment is a U.S. Army aviation unit known for providing helicopter-based air assault, reconnaissance, and support capabilities to ground forces.
|
E927038
|
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: 3rd Aviation Regiment | Statement: [4th Battalion, 3rd Aviation Regiment, partOf, 3rd Aviation Regiment]
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: 3rd Aviation Regiment Triple: [4th Battalion, 3rd Aviation Regiment, partOf, 3rd Aviation Regiment]
Generated description
The 3rd Aviation Regiment is a U.S. Army aviation unit known for providing helicopter-based air assault, reconnaissance, and support capabilities to ground forces.
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_69f76eb8d1808190a588af29d8b266d6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd90c33c481908a932edf6adbe5c6 |
completed | May 7, 2026, 6:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41e04a1da081908a114568df484875 |
completed | June 29, 2026, 3:02 a.m. |
| NEDg | Description generation | batch_6a41e41c47a4819080aad7cc077b3210 |
completed | June 29, 2026, 3:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41e4bcdaac8190b53381cc8934bfab |
completed | June 29, 2026, 3:21 a.m. |
Created at: May 3, 2026, 4:32 p.m.