Triple
T20907948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christine |
E514855
|
entity |
| Predicate | relationshipToHeed |
P141644
|
FINISHED |
| Object | rivalry |
—
|
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: rivalry | Statement: [Christine, relationshipToHeed, rivalry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHeed Context triple: [Christine, relationshipToHeed, rivalry]
-
A.
relationshipToBond
Indicates the specific type of personal, familial, or professional relationship an entity has to the person named Bond.
-
B.
relationshipImpact
Indicates how one entity’s relationship with another affects or changes those entities or their interaction.
-
C.
relationshipToRa
Indicates a specified type of relational connection that an entity has to the entity Ra.
-
D.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
E.
relationshipToRelative
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
- 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_69e0b4f8a1108190bce3d31331290ced |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6e900ee7881909ab1046b40dca486 |
completed | April 21, 2026, 3:03 a.m. |
| PD | Predicate disambiguation | batch_69e5c9ac91108190a6700fcdf2f11890 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53d22d08190bc17ed4bed53804a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:47 p.m.