Triple
T28249239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clorinda |
E712269
|
entity |
| Predicate | relationshipTypeWithTancredi |
P201273
|
FINISHED |
| Object | love and conflict |
—
|
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: love and conflict | Statement: [Clorinda, relationshipTypeWithTancredi, love and conflict]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithTancredi Context triple: [Clorinda, relationshipTypeWithTancredi, love and conflict]
-
A.
relationshipTypeWithTristan
Indicates the specific nature or category of the relationship that an entity has with Tristan.
-
B.
relationshipToTristan
Indicates the specific type of personal, social, or familial connection that one entity has with the individual named Tristan.
-
C.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
D.
relationshipToCarmen
Indicates the specific type of personal or social relationship an entity has with Carmen.
-
E.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
- 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_69efb51fb98881909692421959ec0170 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69ffe613c03481909f3043ec8bf0bed9 |
completed | May 10, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_69ffe4a73fb4819091600725a443981a |
completed | May 10, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69ffe6130698819099328fce92bb2784 |
completed | May 10, 2026, 1:57 a.m. |
Created at: April 27, 2026, 11:03 p.m.