Triple
T19567114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Rebecca Hall |
E489612
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hall |
—
|
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: Hall | Statement: [Anna Rebecca Hall, familyName, Hall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hall Context triple: [Anna Rebecca Hall, familyName, Hall]
-
A.
Hall
Hall is a small rural village and outer suburb of Canberra in the Australian Capital Territory, known for its historic character and village atmosphere.
-
B.
Hall
chosen
Hall is a common English surname borne by numerous notable individuals across diverse fields such as science, arts, and public life.
-
C.
Hall H
Hall H is the massive, high-profile main presentation hall at San Diego Comic-Con, famed for hosting the convention’s biggest and most anticipated panels and premieres.
-
D.
Hall 5
Hall 5 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
-
E.
Hall 5
Hall 5 is one of the large exhibition and event halls within the International Convention Centre in Birmingham, used for conferences, trade shows, and large-scale gatherings.
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63f784ff88190a515c78429de3caf |
completed | April 20, 2026, 3 p.m. |
Created at: April 10, 2026, 1:42 p.m.