Triple
T33855847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwen West |
E867768
|
entity |
| Predicate | relationshipTypeWithGavinShipman |
P206703
|
FINISHED |
| Object | mother-in-law of wife’s husband |
—
|
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: mother-in-law of wife’s husband | Statement: [Gwen West, relationshipTypeWithGavinShipman, mother-in-law of wife’s husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithGavinShipman Context triple: [Gwen West, relationshipTypeWithGavinShipman, mother-in-law of wife’s husband]
-
A.
relationshipTypeWith Eugene Gant
Indicates the specific nature or category of relationship that an entity has with Eugene Gant.
-
B.
relationshipTypeWith W. O. Gant
Indicates the specific nature or category of relational connection that an entity has with W. O. Gant.
-
C.
relationshipToGabeGoodman
Indicates the specific type of personal or social relationship an entity has with Gabe Goodman.
-
D.
relationshipToJohnShipton
Indicates the specific familial or social relationship that an entity has to John Shipton.
-
E.
relationshipTypeWith Godwin Baxter
Indicates the specific nature or category of relationship that an entity has with Godwin Baxter.
- 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_69f349943ccc8190a3c41a3e0ae46cbf |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037e07fe4481909ca21eae7a941ee7 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 1, 2026, 1:47 a.m.