Triple
T5328778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Grandy |
E123250
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Robert |
E2918
|
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: Robert | Statement: [Robert Grandy, hasGivenName, Robert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Context triple: [Robert Grandy, hasGivenName, Robert]
-
A.
Robert
chosen
Robert is a common masculine given name of Germanic origin, widely used in English-speaking countries.
-
B.
Roy
Roy is a common masculine given name of Celtic origin, often used independently or as a nickname for longer names.
-
C.
Roy
Roy is a suburban city in northern Utah, located near Ogden and part of the Ogden–Clearfield metropolitan area.
-
D.
Thomas
Thomas is the given name of Sir Thomas Frankland, a titled member of the British Frankland family.
-
E.
Thomas
Thomas is the given name of Tom Savini, the renowned American special effects makeup artist, actor, and director known for his groundbreaking work in horror films.
- 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_69bd46477f9081909d242a327d749466 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8593bd6c8190b2054e548ddf2458 |
completed | March 20, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21bebea0819083e6deae67f3e834 |
completed | March 21, 2026, 10:54 p.m. |
Created at: March 20, 2026, 2 p.m.