Triple
T10782857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norma Besant |
E254371
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Norma |
E12662
|
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: Norma | Statement: [Norma Besant, givenName, Norma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norma Context triple: [Norma Besant, givenName, Norma]
-
A.
Norma
chosen
Norma is a feminine given name used in various cultures, often considered the female counterpart of the name Norman.
-
B.
Norma
Norma is a small Italian town in the Lazio region, known for its hilltop setting and proximity to the ancient archaeological site of Norba.
-
C.
Norma
Norma is a small, faint constellation in the southern sky, located between Scorpius and Ara.
-
D.
Norma Major
Norma Major is a British charity campaigner and author best known as the wife of former UK Prime Minister John Major.
-
E.
Judy
"Judy" is a 2019 biographical drama film in which Renée Zellweger portrays legendary entertainer Judy Garland during her final years, a role that earned her widespread acclaim and major acting awards.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732c5618081908f72838ed42ed05c |
completed | April 9, 2026, 5:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de55fbfc70819098eb40cf0d1b9e8c |
completed | April 14, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:17 p.m.