Triple
T2486176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norma Major |
E55931
|
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 Major, givenName, Norma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norma Context triple: [Norma Major, 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 Major
Norma Major is a British charity campaigner and author best known as the wife of former UK Prime Minister John Major.
-
C.
Norma Crane
Norma Crane was an American actress best known for her role as Golde, Tevye’s wife, in the film adaptation of the musical "Fiddler on the Roof."
-
D.
Nora
Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
-
E.
Gloria
Gloria is a central human character in the 2023 film "Barbie," portrayed as a Mattel employee and mother whose personal struggles and imagination help bridge the real world with Barbie Land.
- 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_69ab49e670a88190b928e08302381710 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd17705488190b90b1aa66dd25972 |
completed | March 7, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af17ba433481908eccda5c6c6246be |
completed | March 9, 2026, 6:55 p.m. |
Created at: March 6, 2026, 9:45 p.m.