Triple
T14289562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weapons Company, 2nd Battalion, 5th Marines |
E354271
|
entity |
| Predicate | nicknameOfParentBattalion |
P52282
|
FINISHED |
| Object | 2/5 |
—
|
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: 2/5 | Statement: [Weapons Company, 2nd Battalion, 5th Marines, nicknameOfParentBattalion, 2/5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nicknameOfParentBattalion Context triple: [Weapons Company, 2nd Battalion, 5th Marines, nicknameOfParentBattalion, 2/5]
-
A.
nicknameOfParentUnit
chosen
Indicates that one unit serves as an informal or alternative name (nickname) for its parent unit.
-
B.
hasBattalionNumber
Indicates that an entity (such as a military unit) is associated with a specific battalion number identifier.
-
C.
militaryUnitName
Indicates the specific official name assigned to a military unit in the context of a broader relationship or record.
-
D.
hasBattalion
Indicates that one entity possesses, commands, or is organizationally assigned a specific battalion.
-
E.
numberOfBattalions
Indicates the quantitative relationship specifying how many battalions are associated with a given entity or context.
- 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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6981e9148190baf2ed56a7b7340e |
completed | April 14, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69de2a8f81f08190af737e1654847aa6 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:11 a.m.