Triple
T14121476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Experiment 626 |
E339914
|
entity |
| Predicate | homeWorld |
P4624
|
FINISHED |
| Object | Turo |
E1081175
|
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: Turo | Statement: [Experiment 626, homeWorld, Turo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Turo Context triple: [Experiment 626, homeWorld, Turo]
-
A.
Turo
chosen
Turo is a fictional planet in the Lilo & Stitch universe, known as the technologically advanced homeworld of the alien scientist Dr. Jumba Jookiba.
-
B.
Byfleet
Byfleet is a village and former civil parish in southeast England, situated within the county of Surrey.
-
C.
GrabCar
GrabCar is a ride-hailing service under the Grab platform that connects passengers with private car drivers via a mobile app across Southeast Asia.
-
D.
CarRentals.com
CarRentals.com is an online car rental booking platform that allows users to compare and reserve vehicles from multiple rental companies worldwide.
-
E.
Lyft
Lyft is a major American ride-hailing and transportation company that connects passengers with drivers through a mobile app platform.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de60942a588190beff0058a92f7051 |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7e04184819081633f9cfc0ccab9 |
completed | May 7, 2026, 8:36 p.m. |
Created at: April 9, 2026, 10:22 p.m.