Triple
T5118881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. John Brown |
E115407
|
entity |
| Predicate | relationshipTypeWithTita |
P10690
|
FINISHED |
| Object | supportive suitor |
—
|
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: supportive suitor | Statement: [Dr. John Brown, relationshipTypeWithTita, supportive suitor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithTita Context triple: [Dr. John Brown, relationshipTypeWithTita, supportive suitor]
-
A.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
-
C.
titleRelation
Indicates a relationship where one entity serves as the title, designation, or formal name associated with another entity.
-
D.
basisOfRelationship
Indicates that one entity serves as the foundational reason, cause, or justification for the relationship that exists between two or more entities.
-
E.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
- 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_69bd4442ade0819087b9461f892b206b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77aa68b88190a50dd736a72d2901 |
completed | March 20, 2026, 4:36 p.m. |
Created at: March 20, 2026, 1:42 p.m.