Triple
T10861413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teresa Panza |
E256412
|
entity |
| Predicate | spouseOfCharacterType |
P31663
|
FINISHED |
| Object | squire |
—
|
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: squire | Statement: [Teresa Panza, spouseOfCharacterType, squire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOfCharacterType Context triple: [Teresa Panza, spouseOfCharacterType, squire]
-
A.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
B.
spouseType
chosen
Indicates the specific role or category of a person within a spousal relationship (e.g., husband, wife, partner).
-
C.
spouseOfHead
Indicates that one person is the married partner of the individual who holds the position of head (e.g., head of a household, organization, or state).
-
D.
spouseCharacteristic
Indicates that a particular characteristic, trait, or attribute is associated with a person’s spouse within the relationship.
-
E.
spouseInFamily
Indicates that a person is a spouse (married partner) within the context of a specific family unit.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7515186f08190a5cc388a7d936c4f |
completed | April 9, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69d70d308dfc81908792f98cfb871392 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.