Triple
T13898610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman Manley |
E334158
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Manley |
E824181
|
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: Manley | Statement: [Norman Manley, familyName, Manley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manley Context triple: [Norman Manley, familyName, Manley]
-
A.
Manley
chosen
Manley is a small community located within Hamblen County in the state of Tennessee, United States.
-
B.
Toland
Toland is a surname most notably associated with Gregg Toland, the pioneering American cinematographer renowned for his innovative deep-focus techniques in films like "Citizen Kane."
-
C.
Mackenzell
Mackenzell is a small village in the Hesse region of central Germany.
-
D.
Tilghman
Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
-
E.
Conerly
Conerly is a surname most notably associated with Charlie Conerly, a prominent mid-20th-century American football quarterback.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25d8897881908b770cdb565898d4 |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c720b4988190aee1f1f09877212d |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:15 p.m.