Triple
T24642279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melinda Trenchard |
E610000
|
entity |
| Predicate | marriedToSinceTeenageYears |
P67588
|
FINISHED |
| Object | Tom Jones |
—
|
NE NERFINISHED |
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: Tom Jones | Statement: [Melinda Trenchard, marriedToSinceTeenageYears, Tom Jones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToSinceTeenageYears Context triple: [Melinda Trenchard, marriedToSinceTeenageYears, Tom Jones]
-
A.
marriedInYear
Indicates that two entities are married to each other in a specific calendar year.
-
B.
parentsMarriedAt
Indicates that the parents of the given entity were married at the specified time or date.
-
C.
marriedOn
Indicates that a marriage event took place on a specific date for the related entities.
-
D.
spouseOfSince
chosen
Indicates that two individuals are spouses and specifies the date or time from which their marital relationship has been in effect.
-
E.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another 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_69e2c4d28f848190ac38c400060e943d |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f40db2b5e08190bb55d02a4ceba306 |
completed | May 1, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d2b20881908dcc3b15cbe3e5b0 |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:33 a.m.