Triple
T10365739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lydia Bennet |
E244245
|
entity |
| Predicate | guardianAfterElopement |
P93595
|
FINISHED |
| Object | Mr. Darcy (financially and reputationally intervenes) |
—
|
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: Mr. Darcy (financially and reputationally intervenes) | Statement: [Lydia Bennet, guardianAfterElopement, Mr. Darcy (financially and reputationally intervenes)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: guardianAfterElopement Context triple: [Lydia Bennet, guardianAfterElopement, Mr. Darcy (financially and reputationally intervenes)]
-
A.
bride
Indicates that an entity is a woman who is getting married or has just been married in relation to a wedding event or spouse.
-
B.
nuptialFlightTrigger
Indicates the condition or event that initiates or triggers a nuptial flight between mating individuals.
-
C.
guestAtWedding
Indicates that a person is attending or has attended a particular wedding as a guest.
-
D.
hasPublicCeremony
Indicates that a public ceremony is held or conducted in relation to the subject entity.
-
E.
engagedTo
Indicates that two entities are formally committed to marry each other.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e96f25f48190a41c8b0206b9238c |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dface5508190a7b42f01ad0a19a2 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e944fac4819093b0312aa0efd729 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, noon