Triple
T29746196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes Colomar |
E752756
|
entity |
| Predicate | relationshipToMannyCalavera |
P203588
|
FINISHED |
| Object | love interest |
—
|
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 interest | Statement: [Mercedes Colomar, relationshipToMannyCalavera, love interest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMannyCalavera Context triple: [Mercedes Colomar, relationshipToMannyCalavera, love interest]
-
A.
relationshipToMannyBianco
Indicates the specific type of personal or social relationship an entity has with Manny Bianco.
-
B.
relationshipToCarmen
Indicates the specific type of personal or social relationship an entity has with Carmen.
-
C.
relationshipToCalvero
Indicates the type or nature of the relationship that an entity has with Calvero.
-
D.
relationshipToMiguel
Indicates the nature or type of relationship that one entity has with Miguel.
-
E.
relationshipToMannyMachado
Indicates the specific type of relationship or connection an entity has to Manny Machado.
- 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_69f0d62b064081908c1ae61cd68fb139 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a01ade363088190b3a3e3dedabd1aa2 |
completed | May 11, 2026, 10:22 a.m. |
| PD | Predicate disambiguation | batch_6a01acf3c5788190932bd8fcf27c123d |
completed | May 11, 2026, 10:18 a.m. |
| PDg | Predicate description generation | batch_6a01ade289a48190b05e51bb6232e0f2 |
completed | May 11, 2026, 10:22 a.m. |
Created at: April 28, 2026, 7:51 p.m.