Triple
T1644540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Vanderbilt |
E35549
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gloria |
E126874
|
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: Gloria | Statement: [Gloria Vanderbilt, givenName, Gloria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gloria Context triple: [Gloria Vanderbilt, givenName, Gloria]
-
A.
Gloria
Gloria is an American sitcom centered on Gloria Stivic, the daughter from "All in the Family," as she navigates life as a single mother.
-
B.
Gloria
Gloria is a joyful hymn of praise in Christian liturgy, traditionally sung during major celebrations such as the Easter Vigil.
-
C.
Gloria
chosen
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.
-
D.
Marlene
Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
-
E.
Glamorous Glennis
Glamorous Glennis was the name Chuck Yeager gave to several of his aircraft, most famously the Bell X-1 rocket plane in which he first broke the sound barrier.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa622e9b08819094960b2329c6e7e6 |
completed | March 6, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a26350819087e7a87b52561143 |
completed | March 8, 2026, 11:42 a.m. |
Created at: March 4, 2026, 7:28 p.m.