Triple
T382586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Darcy (Bridget Jones) |
E8711
|
entity |
| Predicate | romanticArc |
P10693
|
FINISHED |
| Object | enemies to lovers with Bridget Jones |
—
|
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: enemies to lovers with Bridget Jones | Statement: [Mark Darcy (Bridget Jones), romanticArc, enemies to lovers with Bridget Jones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanticArc Context triple: [Mark Darcy (Bridget Jones), romanticArc, enemies to lovers with Bridget Jones]
-
A.
loveInterest
Indicates that one entity is the romantic object of affection or attraction for another entity.
-
B.
era
Indicates that something existed, occurred, or was valid during a specified historical or temporal period.
-
C.
fate
Indicates that an entity is destined or predetermined to experience a particular outcome or course of events beyond its control.
-
D.
ally
Indicates a cooperative relationship in which one entity supports, assists, or aligns with another, often for mutual benefit or a shared goal.
-
E.
spouseOrLover
Indicates a romantic partnership between two entities, whether formalized as a spouse or existing as a lover.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec40ff8c81909306eb2dfe1512af |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e96602188190b0cbc167f55a9237 |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea2dc3088190a2aeb4496aff3582 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.