Triple
T24848034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the lamplighter |
E621809
|
entity |
| Predicate | relationshipToLittlePrince |
P162066
|
FINISHED |
| Object | meets and talks with the little prince |
—
|
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: meets and talks with the little prince | Statement: [the lamplighter, relationshipToLittlePrince, meets and talks with the little prince]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLittlePrince Context triple: [the lamplighter, relationshipToLittlePrince, meets and talks with the little prince]
-
A.
relationshipToPrincess
Indicates the specific familial, social, or romantic connection that one entity has to a princess.
-
B.
relationshipToKlaatu
Indicates the specific familial, social, or professional relationship that one entity has to the entity named Klaatu.
-
C.
relationshipToPete
Indicates the specific type of relationship or connection that an entity has to Pete.
-
D.
relationshipToHumans
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
E.
relationshipToCreature
Indicates a specified type of relational connection that one entity has toward a particular creature.
- 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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f622abdfac8190988421c946411d7e |
completed | May 2, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69f620dc38088190b56b2b15ed75b3c2 |
completed | May 2, 2026, 4:05 p.m. |
| PDg | Predicate description generation | batch_69f621fbfc2c8190bfa802d7dc0f6aa4 |
completed | May 2, 2026, 4:10 p.m. |
Created at: April 18, 2026, 5:20 a.m.