Triple
T37752563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Cooper (Spy) |
E941019
|
entity |
| Predicate | relationshipToBradleyFine |
P204375
|
FINISHED |
| Object | co-worker |
—
|
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: co-worker | Statement: [Susan Cooper (Spy), relationshipToBradleyFine, co-worker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBradleyFine Context triple: [Susan Cooper (Spy), relationshipToBradleyFine, co-worker]
-
A.
relationshipToFlagg
Indicates the specific type of personal or social relationship an entity has with the person or entity named Flagg.
-
B.
hasRelationshipTypeWith Fran Fine
Indicates that an entity is connected to Fran Fine by a specific, categorized type of relationship (e.g., familial, professional, romantic, or social).
-
C.
relationshipToStevens
Indicates a specified type of relationship that an entity has to the person or entity named Stevens.
-
D.
relationshipToJewelBundren
Indicates the specific familial or interpersonal connection that one entity has to Jewel Bundren.
-
E.
relationshipTypeWithWaldoLydecker
Indicates the specific nature or category of relationship that an entity has with Waldo Lydecker.
- 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_69f76ee1f3a88190834e6c8af99bccc9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.