Triple
T28340003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max Ferraro |
E717782
|
entity |
| Predicate | relationshipTypeWithPenelopeAlvarez |
P134551
|
FINISHED |
| Object | on-and-off romantic relationship |
—
|
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: on-and-off romantic relationship | Statement: [Max Ferraro, relationshipTypeWithPenelopeAlvarez, on-and-off romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithPenelopeAlvarez Context triple: [Max Ferraro, relationshipTypeWithPenelopeAlvarez, on-and-off romantic relationship]
-
A.
relationToPenelope
chosen
Indicates a relational connection that one entity has specifically toward Penelope.
-
B.
relationshipTypeWithAlmaBelasco
Indicates the specific nature or category of relationship an entity has with Alma Belasco.
-
C.
relationshipTypeWithBlancaTrueba
Indicates that the subject has a specific type of relationship with Blanca Trueba.
-
D.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
E.
relationshipTypeWithEvePolastri
Indicates the specific nature or category of relationship that an entity has with Eve Polastri.
- 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_69eff6eb30388190b898b96c4be6f49d |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69fff4530f908190afe9387f732c2b7e |
completed | May 10, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69fff3c01a64819091196875b0c88607 |
completed | May 10, 2026, 2:56 a.m. |
Created at: April 28, 2026, 12:39 a.m.