Triple
T34753471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sonia Ganguli |
E1001849
|
entity |
| Predicate | hasOriginInStory |
P32195
|
FINISHED |
| Object | United States |
—
|
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: United States | Statement: [Sonia Ganguli, hasOriginInStory, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginInStory Context triple: [Sonia Ganguli, hasOriginInStory, United States]
-
A.
hasOriginalStory
Indicates that one entity serves as the original narrative source or story upon which the other entity is based or derived.
-
B.
hasNarrativeOrigin
Indicates that something originates from, is derived from, or is based on a particular narrative or story.
-
C.
hasTimeInStory
Indicates that a story element, event, or entity occurs or is present during a specific time or time interval within the narrative.
-
D.
hasOriginStoryLocation
chosen
Indicates that an entity’s origin story takes place at or is associated with a specific location.
-
E.
hasSiblingInStory
Indicates that one character in a narrative has at least one sibling who also appears within the same story.
- 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_69f76db0fb30819096709d43f9a1f45f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fea5e828cc8190a9b755a645dc56d2 |
completed | May 9, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69fea36443f08190b2aced9b4a0525fd |
completed | May 9, 2026, 3 a.m. |
Created at: May 3, 2026, 3:59 p.m.