Triple
T37927459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Professor Membrane |
E946129
|
entity |
| Predicate | relationshipToDib |
P94755
|
FINISHED |
| Object | father |
—
|
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: father | Statement: [Professor Membrane, relationshipToDib, father]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToDib Context triple: [Professor Membrane, relationshipToDib, father]
-
A.
relationToITF
Indicates a relationship or association that an entity has with the ITF (International Tennis Federation), such as affiliation, governance, or regulatory connection.
-
B.
relationshipToRelative
chosen
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
-
C.
relationshipToBond
Indicates the specific type of personal, familial, or professional relationship an entity has to the person named Bond.
-
D.
relationToBabo
Indicates a relationship or connection that an entity has to the person or entity referred to as "Babo."
-
E.
relationToBPP
Indicates the specific type of relationship or association an entity has to a designated BPP (e.g., as owner, member, participant, or related party).
- 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_69f76ef3b7248190892fb9706423be7c |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:20 p.m.