Triple
T21275586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Network |
E524378
|
entity |
| Predicate | hasRelationshipTypeWithBalducci |
P143471
|
FINISHED |
| Object | professional association |
—
|
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: professional association | Statement: [The Network, hasRelationshipTypeWithBalducci, professional association]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithBalducci Context triple: [The Network, hasRelationshipTypeWithBalducci, professional association]
-
A.
hasRelationshipTypeWithNenaDaconte
Indicates that there exists a specific type of relationship between an entity and Nena Daconte.
-
B.
hasRelationshipTypeWith Valère
Indicates that an entity stands in a specific, characterized type of relationship with Valère.
-
C.
hasRelationshipTypeWithBenBoykewich
Indicates that an entity has a specific type of relationship or connection with Ben Boykewich.
-
D.
hasRelationshipTypeWithOmar
Indicates that an entity stands in a specified type of interpersonal or associative relationship with Omar.
-
E.
relationshipTypeWithSassi
Indicates the specific type or nature of the relationship that an entity has with Sassi.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e736577fd48190a0038a6ac5678668 |
completed | April 21, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69e5f6161dac8190b06009cd180e3ff7 |
completed | April 20, 2026, 9:47 a.m. |
| PDg | Predicate description generation | batch_69e5f9943ed881909ef49045c5bcf6df |
completed | April 20, 2026, 10:01 a.m. |
Created at: April 16, 2026, 4:02 p.m.