Triple
T37138268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fara Sherazi |
E920031
|
entity |
| Predicate | hasAffiliationInStory |
P187183
|
FINISHED |
| Object | CIA Islamabad Station |
—
|
NE NERFINISHED |
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: CIA Islamabad Station | Statement: [Fara Sherazi, hasAffiliationInStory, CIA Islamabad Station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAffiliationInStory Context triple: [Fara Sherazi, hasAffiliationInStory, CIA Islamabad Station]
-
A.
networkAffiliationInStory
Indicates that, within the context of a story, an entity is affiliated with or belongs to a particular network (such as a media, broadcast, or organizational network).
-
B.
hasStaffTypeInStory
Indicates that a story involves or is associated with a particular type or category of staff.
-
C.
hasDepartmentInStory
Indicates that a particular story includes or is associated with a specific department.
-
D.
associatedWithPersonInStory
Indicates that one entity has a connection or involvement with a specific person within the context of a story.
-
E.
hasManagerInStory
Indicates that one entity serves as the manager of another entity within the context of a specific story or narrative.
- 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_69f76e9e9d008190a250b0387c992c74 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
| PDg | Predicate description generation | batch_69fb344ba5408190a6fe8face293d88b |
completed | May 6, 2026, 12:30 p.m. |
Created at: May 3, 2026, 4:15 p.m.