Triple
T18690318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willie Gingrich |
E456977
|
entity |
| Predicate | hasFamilyInStory |
P38221
|
FINISHED |
| Object | sister-in-law of Harry Hinkle (through his wife) |
—
|
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: sister-in-law of Harry Hinkle (through his wife) | Statement: [Willie Gingrich, hasFamilyInStory, sister-in-law of Harry Hinkle (through his wife)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamilyInStory Context triple: [Willie Gingrich, hasFamilyInStory, sister-in-law of Harry Hinkle (through his wife)]
-
A.
hasSiblingInStory
Indicates that one character in a narrative has at least one sibling who also appears within the same story.
-
B.
hasAllyInStory
Indicates that one entity is portrayed as an ally or supportive partner of another entity within the context of a specific story or narrative.
-
C.
hasFamilyRole
chosen
Indicates that one entity holds a specific familial role or position in relation to another entity.
-
D.
hasFictionalFamily
Indicates that an entity is associated with a family that exists only within a fictional or imaginary context.
-
E.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
- 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_69d8d391eb488190ac2e9abf5bf255e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e562e28e5c8190b0033c1667d50e05 |
completed | April 19, 2026, 11:18 p.m. |
| PD | Predicate disambiguation | batch_69e478de85088190ba5f005f1d39f587 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:49 a.m.