Triple
T595117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M3 motorway |
E17362
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object |
Hook
Hook is a village in Hampshire, England, known as a commuter settlement with good transport links to nearby towns and London.
|
E74486
|
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: Hook | Statement: [M3 motorway, passesNear, Hook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hook Context triple: [M3 motorway, passesNear, Hook]
-
A.
Hop
Hop is a regional contactless transit fare system used for paying fares across multiple public transportation agencies in the Portland–Vancouver metropolitan area.
-
B.
Kick
Kick was the affectionate nickname of Kathleen Kennedy Cavendish, the socially prominent and charismatic sister of U.S. President John F. Kennedy.
-
C.
Looper
Looper is a 2012 science-fiction action film directed by Rian Johnson, known for its time-travel premise and starring Joseph Gordon-Levitt and Bruce Willis.
-
D.
Grab
Grab is a Southeast Asian super-app company best known for its ride-hailing, food delivery, and digital payments services.
-
E.
Loop
The Loop is Chicago’s central business district and downtown core, known for its dense cluster of skyscrapers, cultural institutions, and historic elevated train system.
- 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: Hook Triple: [M3 motorway, passesNear, Hook]
Generated description
Hook is a village in Hampshire, England, known as a commuter settlement with good transport links to nearby towns and London.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hook Target entity description: Hook is a village in Hampshire, England, known as a commuter settlement with good transport links to nearby towns and London.
-
A.
Hop
Hop is a regional contactless transit fare system used for paying fares across multiple public transportation agencies in the Portland–Vancouver metropolitan area.
-
B.
Kick
Kick was the affectionate nickname of Kathleen Kennedy Cavendish, the socially prominent and charismatic sister of U.S. President John F. Kennedy.
-
C.
Looper
Looper is a 2012 science-fiction action film directed by Rian Johnson, known for its time-travel premise and starring Joseph Gordon-Levitt and Bruce Willis.
-
D.
Grab
Grab is a Southeast Asian super-app company best known for its ride-hailing, food delivery, and digital payments services.
-
E.
Loop
The Loop is Chicago’s central business district and downtown core, known for its dense cluster of skyscrapers, cultural institutions, and historic elevated train system.
- 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bd280ac8190b6a530ce73da85c8 |
completed | March 1, 2026, 8:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a518c61744819090ccc037d61a61b1 |
completed | March 2, 2026, 4:57 a.m. |
| NEDg | Description generation | batch_69a5197869fc8190b3e11f46f4eea7b9 |
completed | March 2, 2026, 5 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a519e6319c81908d49076dfe2cd963 |
completed | March 2, 2026, 5:02 a.m. |
Created at: March 1, 2026, 7:33 p.m.