Triple
T21805875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Booth Playhouse |
E538347
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Charlotte |
—
|
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: Charlotte | Statement: [Booth Playhouse, city, Charlotte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charlotte Context triple: [Booth Playhouse, city, Charlotte]
-
A.
Charlotte
Charlotte is a feminine given name of French and English origin, traditionally used as the female form of Charles and borne by numerous queens, nobles, and notable figures.
-
B.
Charlotte
Charlotte is a royal figure bearing the traditional title of Princess Royal, historically associated with the eldest daughter of the British monarch.
-
C.
Charlotte
Charlotte is a central character in the 2015 comedy film "The Overnight," involved in the awkward and revealing interactions that unfold over the course of a single evening.
-
D.
Charlotte
Charlotte is a small town located in Atascosa County in the U.S. state of Texas.
-
E.
Charlotte
chosen
Charlotte is a major city in North Carolina known for its financial industry, rapid growth, and cultural institutions.
- 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_69e0c473f0f8819086c9d1b4a143bd67 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f07802bffc8190b383a2c89de75f74 |
completed | April 28, 2026, 9:04 a.m. |
Created at: April 16, 2026, 6:53 p.m.