Triple
T7795556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2nd Battalion, 506th Infantry Regiment |
E180289
|
entity |
| Predicate | hasRegimentalNumber |
P18684
|
FINISHED |
| Object | 506th |
—
|
LITERAL 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: 506th | Statement: [2nd Battalion, 506th Infantry Regiment, hasRegimentalNumber, 506th]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegimentalNumber Context triple: [2nd Battalion, 506th Infantry Regiment, hasRegimentalNumber, 506th]
-
A.
regimentalNumber
chosen
Indicates the unique identifying number assigned to a member of a regiment, linking an individual to their specific regimental record.
-
B.
hasRegimentalIdentity
Indicates that an entity possesses or is associated with a specific regimental identity, such as belonging to or being characterized by a particular regiment.
-
C.
militaryServiceNumber
Indicates the unique identification number assigned to an individual for tracking their record in a military service context.
-
D.
hasBattalionNumber
Indicates that an entity (such as a military unit) is associated with a specific battalion number identifier.
-
E.
airForceServiceNumber
Indicates the unique identification number assigned to an individual for their service in an air force.
- F. None of above.
Provenance (3 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:31 p.m.