Triple
T37876682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mazer Rackham |
E944743
|
entity |
| Predicate | relationshipToEnder |
P204442
|
FINISHED |
| Object | teacher |
—
|
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: teacher | Statement: [Mazer Rackham, relationshipToEnder, teacher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToEnder Context triple: [Mazer Rackham, relationshipToEnder, teacher]
-
A.
relationshipToEdd
Indicates the specific type of relationship or connection that an entity has to Edd.
-
B.
relationshipEnd
Indicates that a previously existing relationship between entities has been terminated or has come to an end.
-
C.
relationshipToEva
Indicates a specified type of personal or social relationship that an entity has with Eva.
-
D.
relationshipToKlaatu
Indicates the specific familial, social, or professional relationship that one entity has to the entity named Klaatu.
-
E.
relationshipToKeter
Indicates a relationship in which an entity is connected or related to the concept, object, or category referred to as "Keter."
- 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_69f76eef55d481908ca6660b4b532550 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.