Triple
T24642280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melinda Trenchard |
E610000
|
entity |
| Predicate | relationshipTypeWithTomJones |
P156881
|
FINISHED |
| Object | wife |
—
|
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: wife | Statement: [Melinda Trenchard, relationshipTypeWithTomJones, wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithTomJones Context triple: [Melinda Trenchard, relationshipTypeWithTomJones, wife]
-
A.
relationshipTypeWith Jessa Johansson
Indicates the specific nature or category of relationship that an entity has with Jessa Johansson.
-
B.
relationshipWithTomWambsgans
Indicates the existence and nature of a relationship or connection that an entity has with Tom Wambsgans.
-
C.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipTypeWithJakeBarnes
Indicates the specific nature or category of relationship that an entity has with Jake Barnes.
-
E.
relationshipTypeWithJohnLuther
Indicates the specific nature or category of the relationship an entity has with John Luther.
- 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_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. |
| PDg | Predicate description generation | batch_69f40b2f49688190aa1217b7a456dd63 |
completed | May 1, 2026, 2:08 a.m. |
Created at: April 18, 2026, 2:33 a.m.