Triple
T2883646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafael Benítez |
E59454
|
entity |
| Predicate | playedFor |
P2170
|
FINISHED |
| Object | Parla |
E95405
|
NE 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: Parla | Statement: [Rafael Benítez, playedFor, Parla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parla Context triple: [Rafael Benítez, playedFor, Parla]
-
A.
Parla
chosen
Parla is a suburban municipality and residential town located in the southern metropolitan area of Madrid, Spain.
-
B.
Veron
Veron is a microbiologist credited with formally naming and classifying the bacterial genus Campylobacter.
-
C.
Nápoles
Nápoles is a middle- to upper-class residential and commercial neighborhood in Mexico City, known for its modern architecture, the World Trade Center complex, and a mix of offices, restaurants, and apartment buildings.
-
D.
Melfi
Melfi is a historic town in southern Italy known as an important medieval center of Norman rule and site of several papal councils.
-
E.
Cagli
Cagli is a historic town in Italy’s Marche region, known for its medieval architecture and scenic setting in the Apennine foothills.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe02e0ec48190b969ed921d179560 |
completed | March 7, 2026, 8:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03167d7dc819093e91e0d42f3de6f |
completed | March 10, 2026, 2:57 p.m. |
Created at: March 6, 2026, 10:03 p.m.