Triple
T3596992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Julian Amery |
E76163
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Harold |
E9958
|
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: Harold | Statement: [Harold Julian Amery, givenName, Harold]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harold Context triple: [Harold Julian Amery, givenName, Harold]
-
A.
Harold
chosen
Harold is a masculine given name of Old English origin, historically borne by several notable figures including kings and modern public personalities.
-
B.
Gerald
Gerald is the birth name of Jerry Brown, the longtime Democratic politician and former governor of California.
-
C.
Gerald
Gerald is the middle name of Stephen G. Breyer, a former Associate Justice of the United States Supreme Court.
-
D.
Gerald
Gerald is a masculine given name of Germanic origin, commonly used in English-speaking countries.
-
E.
Reginald
Reginald is a masculine given name of English origin that has been borne by various notable figures, including military officers, politicians, and artists.
- 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_69ad85d8042081908af94a04c410dec0 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc19b26bc8190b3cc0613a1a98361 |
completed | March 8, 2026, 6:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b403130cf081909bb90800d7dc2d6f |
completed | March 13, 2026, 12:29 p.m. |
Created at: March 8, 2026, 3:22 p.m.