Triple
T10460253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AsiaWorld-Expo |
E246651
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Hall 8
Hall 8 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
|
E873545
|
NE FINISHED |
How this triple was built (4 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: Hall 8 | Statement: [AsiaWorld-Expo, hasPart, Hall 8]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hall 8 Context triple: [AsiaWorld-Expo, hasPart, Hall 8]
-
A.
Hall 7
Hall 7 is one of the exhibition and event halls within the AsiaWorld-Expo convention and exhibition center in Hong Kong.
-
B.
Hall 5
Hall 5 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
-
C.
Hall H
Hall H is the massive, high-profile main presentation hall at San Diego Comic-Con, famed for hosting the convention’s biggest and most anticipated panels and premieres.
-
D.
Hall 3
Hall 3 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
-
E.
Hall 2
Hall 2 is one of the exhibition and event halls within the AsiaWorld-Expo convention and exhibition center in Hong Kong.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hall 8 Triple: [AsiaWorld-Expo, hasPart, Hall 8]
Generated description
Hall 8 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hall 8 Target entity description: Hall 8 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
-
A.
Hall 7
Hall 7 is one of the exhibition and event halls within the AsiaWorld-Expo convention and exhibition center in Hong Kong.
-
B.
Hall 5
Hall 5 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
-
C.
Hall H
Hall H is the massive, high-profile main presentation hall at San Diego Comic-Con, famed for hosting the convention’s biggest and most anticipated panels and premieres.
-
D.
Hall 3
Hall 3 is one of the exhibition and event halls within the AsiaWorld-Expo convention and entertainment complex in Hong Kong.
-
E.
Hall 2
Hall 2 is one of the main concourse areas within Paris’s Gare de Lyon railway station, serving passengers with platforms, services, and amenities.
- F. None of above. chosen
Provenance (5 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50882eb0c8190a4311634b867eab1 |
completed | April 7, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e5749208190b37848f0945bd92d |
completed | April 10, 2026, 8:32 p.m. |
| NEDg | Description generation | batch_69d95f80d0c48190b88e3a4b3e42279c |
completed | April 10, 2026, 8:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9602748608190b0c971accf44b7aa |
completed | April 10, 2026, 8:40 p.m. |
Created at: April 6, 2026, 12:18 p.m.