Triple
T10285649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hattie McDaniel |
E241219
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hattie |
E241219
|
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: Hattie | Statement: [Hattie McDaniel, givenName, Hattie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hattie Context triple: [Hattie McDaniel, givenName, Hattie]
-
A.
Hattie
chosen
Hattie is a feminine given name most famously borne by Hattie McDaniel, the first African American to win an Academy Award.
-
B.
Phyllis
Phyllis is a tragic figure from classical legend, often depicted as a wronged lover who is transformed into an almond tree after being abandoned.
-
C.
Phyllis
Phyllis is a 1970s American television sitcom, spun off from The Mary Tyler Moore Show, that stars Cloris Leachman as the widowed Phyllis Lindstrom starting a new life in San Francisco.
-
D.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
E.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2b737788190bfadd0d48ad38f5b |
completed | April 7, 2026, 9:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f8444c48819095100c6d1d45ccc7 |
completed | April 9, 2026, 12:52 a.m. |
Created at: April 6, 2026, 11:40 a.m.