Triple
T34818855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vera Stravinsky |
E1003710
|
entity |
| Predicate | spouseOrderRelativeToIgorStravinsky |
P4764
|
FINISHED |
| Object | second |
—
|
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: second | Statement: [Vera Stravinsky, spouseOrderRelativeToIgorStravinsky, second]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOrderRelativeToIgorStravinsky Context triple: [Vera Stravinsky, spouseOrderRelativeToIgorStravinsky, second]
-
A.
marriedToIgorStravinskyFrom
Indicates that an entity was married to Igor Stravinsky starting from a specified point in time.
-
B.
spouseOrder
chosen
Indicates the position or sequence of a person among multiple spouses in a marital relationship.
-
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.
marriageOrderWithFrankSinatra
Indicates the sequence or order in which an entity was married relative to Frank Sinatra (e.g., first, second, etc.).
-
E.
motherSpouseOrder
Indicates that the subject is the spouse of the object’s mother, with an ordering or ranking among multiple such spouses.
- 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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fe1fd637c08190aa95cd2478c278cb |
completed | May 8, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69fe19344bb481909b5e2144155e4add |
completed | May 8, 2026, 5:11 p.m. |
Created at: May 3, 2026, 4 p.m.