Triple
T12317271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | In Another Country |
E293632
|
entity |
| Predicate | settingLocationType |
P104439
|
FINISHED |
| Object | military hospital |
—
|
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: military hospital | Statement: [In Another Country, settingLocationType, military hospital]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingLocationType Context triple: [In Another Country, settingLocationType, military hospital]
-
A.
typicalUseLocation
Indicates the usual or most common location where an entity is used or operates.
-
B.
placementType
Indicates the specific manner or category in which something is positioned, arranged, or assigned within a given context.
-
C.
setLocation
Indicates assigning or updating the place or position where an entity is located.
-
D.
modelingLocation
Indicates the place or setting where the modeling activity or process occurs.
-
E.
controlsLocation
Indicates that one entity has authority over, manages, or determines the status or use of a particular location.
- F. None of above. chosen
Provenance (4 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec5be788190b82d2edc6a0f1095 |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d93f607a88819089e89fd263ae9937 |
completed | April 10, 2026, 6:20 p.m. |
Created at: April 8, 2026, 9:53 p.m.