Triple
T10832941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentine 7th Infantry Regiment |
E255668
|
entity |
| Predicate | casualtyType |
P26558
|
FINISHED |
| Object | killed in action |
—
|
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: killed in action | Statement: [Argentine 7th Infantry Regiment, casualtyType, killed in action]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtyType Context triple: [Argentine 7th Infantry Regiment, casualtyType, killed in action]
-
A.
casualtiesType
chosen
Indicates the specific category or nature of casualties (e.g., killed, injured, missing) associated with an event or incident.
-
B.
shipTypeInvolved
Indicates that a particular type or class of ship is involved or participates in a specified event, situation, or relationship.
-
C.
craftType
Indicates the specific kind or category of craft or vessel associated with an entity.
-
D.
naveType
Indicates the architectural or structural type or style of a building’s nave in relation to that nave.
-
E.
convoyType
Indicates the specific kind or classification of convoy associated with or used in the relationship between the entities.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7442439dc8190af59f9c8d0637c01 |
completed | April 9, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69d70d25280c8190b648d7d1958b413a |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:19 p.m.