Triple
T24888058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Mott |
E622911
|
entity |
| Predicate | relationshipToTomMarshall |
P165803
|
FINISHED |
| Object | childhood friend |
—
|
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: childhood friend | Statement: [Dan Mott, relationshipToTomMarshall, childhood friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToTomMarshall Context triple: [Dan Mott, relationshipToTomMarshall, childhood friend]
-
A.
relationshipWithTomWambsgans
Indicates the existence and nature of a relationship or connection that an entity has with Tom Wambsgans.
-
B.
hasPoliticalRelationshipWith
Indicates a political connection or association between two entities, such as alliances, rivalries, collaborations, or other forms of political interaction.
-
C.
relationshipWithAnnShankland
Indicates that an entity has some form of personal or professional relationship with Ann Shankland.
-
D.
relationshipToTruman
Indicates the specific familial, social, or professional connection that an entity has with Truman.
-
E.
relationshipToMariane
Indicates the specific type of relationship or connection that an entity has to Mariane.
- 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_69e2fac4aa848190b3446a3922cec150 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f65b14512c8190a40e70319dcc54cd |
completed | May 2, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
| PDg | Predicate description generation | batch_69f65a9cb0bc8190bf8a9b319900bad5 |
completed | May 2, 2026, 8:12 p.m. |
Created at: April 18, 2026, 5:25 a.m.