Triple
T27259148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karuna in Kapurush |
E687708
|
entity |
| Predicate | hasPastRelationshipStatus |
P143101
|
FINISHED |
| Object | unfulfilled romance |
—
|
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: unfulfilled romance | Statement: [Karuna in Kapurush, hasPastRelationshipStatus, unfulfilled romance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPastRelationshipStatus Context triple: [Karuna in Kapurush, hasPastRelationshipStatus, unfulfilled romance]
-
A.
formerRelationshipStatus
chosen
Indicates that a relationship between entities existed in the past but no longer holds in the present.
-
B.
wasInRelationshipDuring
Indicates that two entities were in a romantic or otherwise defined interpersonal relationship with each other during a specified time period.
-
C.
hadPartner
Indicates that an entity was in a romantic or life-partner relationship with another entity at some point in time.
-
D.
hadPartnerType
Indicates that an entity was associated with another entity in a specific type or category of partnership.
-
E.
marriedBefore
Indicates that one entity entered into a marriage at an earlier time than the other entity.
- 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_69ef35567e808190a94458cd44ebff0c |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: April 27, 2026, 10:51 a.m.