Triple
T34500247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Errol |
E885729
|
entity |
| Predicate | relationshipWithEarlOfDorincourt |
P205448
|
FINISHED |
| Object | daughter-in-law |
—
|
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: daughter-in-law | Statement: [Mrs. Errol, relationshipWithEarlOfDorincourt, daughter-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithEarlOfDorincourt Context triple: [Mrs. Errol, relationshipWithEarlOfDorincourt, daughter-in-law]
-
A.
relationshipToEarlJr
Indicates the specific familial or social relationship that an entity has to Earl Jr.
-
B.
titleHolderRelationshipToEarl
Indicates the familial or social relationship that the current title holder has to the Earl.
-
C.
relationshipWithLouisWinthorpeIII
Indicates the existence or nature of a relationship between an entity and Louis Winthorpe III.
-
D.
relationshipToMortimer
Indicates the specific familial, social, or interpersonal connection that an entity has to Mortimer.
-
E.
relationshipToEarlHickey
Indicates the nature or type of personal relationship an entity has with Earl Hickey.
- 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_69f349cc0220819081f154c6964f4dc2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 2:01 a.m.