Triple
T4459566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Smallville |
E98217
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Lana Lang |
E248308
|
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: Lana Lang | Statement: [Smallville, character, Lana Lang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lana Lang Context triple: [Smallville, character, Lana Lang]
-
A.
Lana Lang
chosen
Lana Lang is a longtime supporting character in the Superman franchise, best known as Clark Kent’s childhood friend and early love interest from Smallville.
-
B.
Amanda Reed
Amanda Reed was the benefactor whose bequest and vision led to the establishment of Reed College in Portland, Oregon.
-
C.
Sharon Maguire
Sharon Maguire is a British film director best known for helming the hit romantic comedy "Bridget Jones’s Diary."
-
D.
Lucy Lane
Lucy Lane is a supporting character in DC Comics, best known as Lois Lane’s younger sister and often depicted as a love interest of Jimmy Olsen.
-
E.
Gwen Cooper
Gwen Cooper is a compassionate yet tough Welsh police officer who becomes a key member of the secret alien-hunting team in the British sci-fi series Torchwood.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567184f481908a2787e4ac9bb345 |
completed | March 13, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6283811f0819095aa671ac593bd8d |
completed | March 15, 2026, 3:32 a.m. |
Created at: March 12, 2026, 11:33 p.m.