Triple
T1238654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip Morrison |
E26606
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Morrison |
E127552
|
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: Morrison | Statement: [Philip Morrison, familyName, Morrison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Morrison Context triple: [Philip Morrison, familyName, Morrison]
-
A.
Morrison
chosen
Morrison is a common English and Scottish surname borne by numerous notable figures in politics, sports, arts, and public life.
-
B.
Toni Morrison
Toni Morrison was a renowned American novelist, essayist, and professor celebrated for her powerful explorations of Black American life and identity in works such as "Beloved" and "Song of Solomon."
-
C.
Sula
Sula is a 1973 novel by American author Toni Morrison that explores Black female friendship, community, and identity in a small Ohio town.
-
D.
Shirley
Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
-
E.
Shirley
Shirley is the given name of Shirley Ann Jackson, a prominent American physicist and trailblazing academic leader.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf406b988190a12aa26bbcb88d6a |
completed | March 1, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8f7755fc819089e23eca81583885 |
completed | March 7, 2026, 8:49 p.m. |
Created at: March 1, 2026, 7:47 p.m.