Triple
T15048496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orbost |
E379291
|
entity |
| Predicate | hasNearbyTown |
P3883
|
FINISHED |
| Object |
Marlo
Marlo is a small coastal town in East Gippsland, Victoria, Australia, known for its location near the mouth of the Snowy River and its fishing and outdoor recreation.
|
E1134029
|
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: Marlo | Statement: [Orbost, hasNearbyTown, Marlo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marlo Context triple: [Orbost, hasNearbyTown, Marlo]
-
A.
Marlo
Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
-
B.
Marlohe
Marlohe is the surname of French actress and model Bérénice Marlohe, best known for her role as Sévérine in the James Bond film "Skyfall."
-
C.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
D.
Marylou
Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
-
E.
Marli
Marli is a given name commonly used as a feminine first name in various cultures.
- 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: Marlo Triple: [Orbost, hasNearbyTown, Marlo]
Generated description
Marlo is a small coastal town in East Gippsland, Victoria, Australia, known for its location near the mouth of the Snowy River and its fishing and outdoor recreation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marlo Target entity description: Marlo is a small coastal town in East Gippsland, Victoria, Australia, known for its location near the mouth of the Snowy River and its fishing and outdoor recreation.
-
A.
Marlo
Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
-
B.
Marlohe
Marlohe is the surname of French actress and model Bérénice Marlohe, best known for her role as Sévérine in the James Bond film "Skyfall."
-
C.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
D.
Marylou
Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
-
E.
Marli
Marli is a given name commonly used as a feminine first name in various cultures.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda8e64e48190873104a02a676ff3 |
completed | April 15, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9de83b648190ba437c0fac164ec6 |
completed | May 9, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fe9e6ec16081909cc55fd11bc89eb4 |
completed | May 9, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9eedca1481908ce438991184d62e |
completed | May 9, 2026, 2:41 a.m. |
Created at: April 10, 2026, 3 a.m.