Triple
T241756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airbnb |
E4944
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | Airbnb.org |
E4944
|
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: Airbnb.org | Statement: [Airbnb, hasService, Airbnb.org]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Airbnb.org Context triple: [Airbnb, hasService, Airbnb.org]
-
A.
Airbnb
chosen
Airbnb is a global online marketplace that connects people seeking short-term lodging or experiences with hosts offering accommodations and activities in locations around the world.
-
B.
Expedia Group
Expedia Group is a leading American online travel and technology company that operates numerous global travel fare aggregators and travel metasearch engines.
-
C.
Jet2.com
Jet2.com is a British low-cost leisure airline that operates scheduled and charter flights across Europe from multiple UK bases.
-
D.
Universal Destinations & Experiences
Universal Destinations & Experiences is the theme park and resort division of NBCUniversal that develops and operates Universal-branded entertainment destinations 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25cee6f208190b996be4faa700910 |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3736ff4388190b6b43a149d0003c5 |
completed | Feb. 28, 2026, 11 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.