Triple
T27334789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Maddern |
E689910
|
entity |
| Predicate | spouseOrderForJackLondon |
P4764
|
FINISHED |
| Object | first 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: first wife | Statement: [Elizabeth Maddern, spouseOrderForJackLondon, first wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOrderForJackLondon Context triple: [Elizabeth Maddern, spouseOrderForJackLondon, first wife]
-
A.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
B.
motherSpouseOrder
Indicates that the subject is the spouse of the object’s mother, with an ordering or ranking among multiple such spouses.
-
C.
marriageOrderRelativeToOrsonWelles
Indicates the position or sequence of a person’s marriage relative to Orson Welles’s own marriages (e.g., earlier, later, or same order).
-
D.
spouseOrderRelativeToGeneRoddenberry
Indicates the position or sequence of a person among Gene Roddenberry’s spouses (e.g., first spouse, second spouse, etc.).
-
E.
hasAuthorSpouse
Indicates that the spouse of the subject entity is the author of the related work or 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_69ef355e5b388190a8fc1eba9b4a6656 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f6f8565134819096aac0175f924a9f |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
Created at: April 27, 2026, 11:40 a.m.