Triple
T37247105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sabina |
E923886
|
entity |
| Predicate | relationshipToMaggieAntrobus |
P205794
|
FINISHED |
| Object | employee |
—
|
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: employee | Statement: [Sabina, relationshipToMaggieAntrobus, employee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMaggieAntrobus Context triple: [Sabina, relationshipToMaggieAntrobus, employee]
-
A.
relationshipToMerricat
Indicates the type or nature of a person or entity’s relationship to Merricat.
-
B.
relationshipToMabel
Indicates the specific familial, social, or interpersonal connection that an entity has with Mabel.
-
C.
relationshipTypeWithMaggieVerver
Indicates the specific nature or category of relationship that an entity has with Maggie Verver.
-
D.
relationshipWithMargo
Indicates that an entity has some form of relationship or connection with Margo.
-
E.
relationshipToAgnes
Indicates the specific familial, social, or other relational connection that one entity has to the person named Agnes.
- 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_69f76eaabb4c819093b751b139dad551 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:15 p.m.