Triple
T18048526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 20 Años |
E431864
|
entity |
| Predicate | includesTrack |
P3284
|
FINISHED |
| Object | Isabel |
—
|
NE NERFINISHED |
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: Isabel | Statement: [20 Años, includesTrack, Isabel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabel Context triple: [20 Años, includesTrack, Isabel]
-
A.
Isabel
chosen
Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
-
B.
Isabel
Isabel is a Spanish historical drama television series centered on the life and reign of Queen Isabella I of Castile.
-
C.
Isabelle
Isabelle is a prominent interactive theorem prover and proof assistant widely used in formal verification and mathematical logic research.
-
D.
Isabelle
Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
-
E.
Isabelle
Isabelle is a feminine given name of French origin, commonly used in many countries and cultures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff39394819080407e1614bd5da9 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 10:25 a.m.