Triple
T29529755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Count Almaviva (Il barbiere di Siviglia) |
E749164
|
entity |
| Predicate | relationshipToFigaro |
P203280
|
FINISHED |
| Object | 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: friend | Statement: [Count Almaviva (Il barbiere di Siviglia), relationshipToFigaro, friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFigaro Context triple: [Count Almaviva (Il barbiere di Siviglia), relationshipToFigaro, friend]
-
A.
relationshipToGoriot
Indicates the type or nature of a person's relationship to Goriot.
-
B.
relationshipToMonsieurJourdain
Indicates the type or nature of a person’s relationship to Monsieur Jourdain.
-
C.
relationshipToFelix
Indicates the specific type of relationship or connection that an entity has with Felix.
-
D.
relationshipToHarpagon
Indicates the specific familial or social relationship that an entity has to the character Harpagon.
-
E.
relationshipToJoeCinque
Indicates a specified type of relationship or connection that an entity has to Joe Cinque.
- 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_69f0bd46d99c81908ba9d01cc1dbef7d |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_6a0149447e448190b0480943de0691ee |
completed | May 11, 2026, 3:13 a.m. |
| PD | Predicate disambiguation | batch_6a0148e2c39c8190a3ad6ea6be902c04 |
completed | May 11, 2026, 3:11 a.m. |
| PDg | Predicate description generation | batch_6a014943b7d481909fa2c1c3f176db24 |
completed | May 11, 2026, 3:13 a.m. |
Created at: April 28, 2026, 4:51 p.m.