Triple
T21213650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Multiplus Fidelidade |
E522780
|
entity |
| Predicate | hadPartnerType |
P143578
|
FINISHED |
| Object | airlines |
—
|
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: airlines | Statement: [Multiplus Fidelidade, hadPartnerType, airlines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadPartnerType Context triple: [Multiplus Fidelidade, hadPartnerType, airlines]
-
A.
hadPartner
Indicates that an entity was in a romantic or life-partner relationship with another entity at some point in time.
-
B.
hasExPartner
Indicates that one entity was formerly in a romantic or intimate partnership with another entity, but that relationship has ended.
-
C.
hasPartner
Indicates that one entity is in a partner relationship (such as romantic, life, or business partnership) with another entity.
-
D.
lifePartnerType
Indicates the type or category of a person’s life partner in a long-term or committed relationship.
-
E.
hasConcubineFrom
Indicates that a person has a concubine whose origin or affiliation is from a specified place or source.
- 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_69e0b511ed84819099b449b4a111085c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7347088488190aa764b3f4bbac44d |
completed | April 21, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_69e5f6094e3c81909ee9699e00d371f7 |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5fa92a2448190896c022dd27511ad |
completed | April 20, 2026, 10:06 a.m. |
Created at: April 16, 2026, 3:39 p.m.