Triple
T19935450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helen Benson |
E479162
|
entity |
| Predicate | relationshipToKlaatu |
P137900
|
FINISHED |
| Object | ally |
—
|
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: ally | Statement: [Helen Benson, relationshipToKlaatu, ally]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToKlaatu Context triple: [Helen Benson, relationshipToKlaatu, ally]
-
A.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
B.
relationshipToRelative
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
-
C.
relationshipToRa
Indicates a specified type of relational connection that an entity has to the entity Ra.
-
D.
relationshipToMarkWatney
Indicates the specific type of personal or social relationship an entity has with Mark Watney.
-
E.
relationshipToJabba
Indicates the specific personal or social connection an entity has to Jabba, such as familial, hierarchical, or associative ties.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a173a448190a25cb859803e3afc |
completed | April 20, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69e537f47c508190853c4e009c6b5566 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:53 p.m.