Triple
T36407826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Young Girl and the Monsoon |
E896794
|
entity |
| Predicate | hasDangerousAssignmentAbroad |
P185455
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [The Young Girl and the Monsoon, hasDangerousAssignmentAbroad, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDangerousAssignmentAbroad Context triple: [The Young Girl and the Monsoon, hasDangerousAssignmentAbroad, true]
-
A.
hasCountryOfRisk
Indicates that an entity is associated with a country where it faces significant exposure, vulnerability, or potential risk.
-
B.
hasAssociatedStructureAbroad
Indicates that an entity has a related or connected structural presence located in a foreign country.
-
C.
locatedInOverseasCountry
Indicates that an entity is situated in a country that is overseas relative to a given reference location or jurisdiction.
-
D.
couldServeOverseas
Indicates that an entity has the ability or eligibility to perform service or duties in a foreign or overseas location.
-
E.
livedAbroadAsChild
Indicates that a person spent part of their childhood residing in a foreign country.
- 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_69f76e53b81081908d3b81860593f38a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bcccd7988190aa5c931ff347d33c |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be9b9ab481908328e0e8d8ac73d4 |
completed | May 3, 2026, 9:31 p.m. |
Created at: May 3, 2026, 4:10 p.m.