Triple
T19965606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princes Freeway |
E479923
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Lara |
—
|
NE NERFINISHED |
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: Lara | Statement: [Princes Freeway, passesNear, Lara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Context triple: [Princes Freeway, passesNear, Lara]
-
A.
Lara
Lara is a feminine given name, often used in various cultures and languages, sometimes as a variant of Laura or derived from Latin and Russian origins.
-
B.
Lara
Lara is a semi-autobiographical novel by British writer Bernardine Evaristo that explores themes of identity, heritage, and family across generations.
-
C.
Lara
Lara is a narrative poem by Lord Byron that draws on his experiences and observations from his travels in the Ottoman Empire.
-
D.
Lara
chosen
Lara is a township in Victoria, Australia, situated between Melbourne and Geelong and known as a residential and commuter community with nearby natural attractions.
-
E.
Lara Sanoica
Lara Sanoica is an American local politician who serves as the mayor of Rolling Meadows, Illinois.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc4f47c8190a721f5e488150d81 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.